,
Melissa S. Armstrong [aut] ,
Eric R. Press [aut] ,
Anne-Sophie Bonnet-Lebrun [rev],
Marco Sciaini [rev]| Title: | Wrangle, Analyze, and Visualize Animal Movement Data |
|---|---|
| Description: | Tools to import, clean, and visualize movement data, particularly from motion capture systems such as Optitrack's 'Motive', the Straw Lab's 'Flydra', or from other sources. We provide functions to remove artifacts, standardize tunnel position and tunnel axes, select a region of interest, isolate specific trajectories, fill gaps in trajectory data, and calculate 3D and per-axis velocity. For experiments of visual guidance, we also provide functions that use subject position to estimate perception of visual stimuli. |
| Authors: | Vikram B. Baliga [aut, cre] ,
Melissa S. Armstrong [aut] ,
Eric R. Press [aut] ,
Anne-Sophie Bonnet-Lebrun [rev],
Marco Sciaini [rev] |
| Maintainer: | Vikram B. Baliga <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.7 |
| Built: | 2024-11-27 03:57:05 UTC |
| Source: | https://github.com/ropensci/pathviewr |
Should you have data from a non-Motive, non-Flydra source, this function can be used to ensure your data are put into the right format to work with other pathviewr functions.
as_viewr( obj_name, frame_rate = 100, frame_col, time_col, subject_col, position_length_col, position_width_col, position_height_col, include_rotation = FALSE, rotation_real_col, rotation_length_col, rotation_width_col, rotation_height_col )as_viewr( obj_name, frame_rate = 100, frame_col, time_col, subject_col, position_length_col, position_width_col, position_height_col, include_rotation = FALSE, rotation_real_col, rotation_length_col, rotation_width_col, rotation_height_col )
obj_name |
A tibble or data frame containing movement trajectories |
frame_rate |
Must be a single numeric value indicating capture frame rate in frames per second. |
frame_col |
Column number of obj_name that contains frame numbers |
time_col |
Column number of obj_name that contains time (must be in seconds) |
subject_col |
Column number of obj_name that contains subject name(s) |
position_length_col |
Column number of obj_name that contains length-axis position values |
position_width_col |
Column number of obj_name that contains width-axis position values |
position_height_col |
Column number of obj_name that contains height-axis position values |
include_rotation |
Are rotation data included? Defaults to FALSE |
rotation_real_col |
Column number of obj_name that contains the "real" axis of quaternion rotation data |
rotation_length_col |
Column number of obj_name that contains the length axis of quaternion rotation data |
rotation_width_col |
Column number of obj_name that contains the width axis of quaternion rotation data |
rotation_height_col |
Column number of obj_name that contains the height axis of quaternion rotation data |
A tibble that is organized to be compliant with other
pathviewr functions and that contains the attributes
pathviewr_steps with entries set to c("viewr",
"renamed_tunnel", "gathered_tunnel")
Vikram B. Baliga
Other data import functions:
import_and_clean_batch(),
import_batch(),
read_flydra_mat(),
read_motive_csv()
## Create a dummy data frame with simulated (nonsense) data df <- data.frame(frame = seq(1, 100, by = 1), time_sec = seq(0, by = 0.01, length.out = 100), subject = "birdie_sanders", z = rnorm(100), x = rnorm(100), y = rnorm(100)) ## Use as_viewr() to convert it into a viewr object test <- as_viewr( df, frame_rate = 100, frame_col = 1, time_col = 2, subject_col = 3, position_length_col = 5, position_width_col = 6, position_height_col = 4 )## Create a dummy data frame with simulated (nonsense) data df <- data.frame(frame = seq(1, 100, by = 1), time_sec = seq(0, by = 0.01, length.out = 100), subject = "birdie_sanders", z = rnorm(100), x = rnorm(100), y = rnorm(100)) ## Use as_viewr() to convert it into a viewr object test <- as_viewr( df, frame_rate = 100, frame_col = 1, time_col = 2, subject_col = 3, position_length_col = 5, position_width_col = 6, position_height_col = 4 )
Combine a list of multiple viewr objects into a single viewr object
bind_viewr_objects(obj_list)bind_viewr_objects(obj_list)
obj_list |
A list of viewr objects |
A single viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") that combines all the
rows of the source viewr objects in obj_list. Metadata may not
necessarily be retained and therefore attributes should be used with
caution.
Vikram B. Baliga
Other batch functions:
clean_viewr_batch(),
import_and_clean_batch(),
import_batch()
## Since we only have one example file of each type provided ## in pathviewr, we will simply import the same example multiple ## times to simulate batch importing. Replace the contents of ## the following list with your own list of files to be imported. ## Make a list of the same example file 3x import_list <- c(rep( system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), 3 )) ## Batch import motive_batch_imports <- import_batch(import_list, import_method = "motive", import_messaging = TRUE) ## Batch cleaning of these imported files ## via clean_viewr_batch() motive_batch_cleaned <- clean_viewr_batch( file_announce = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Alternatively, use import_and_clean_batch() to ## combine import with cleaning on a batch of files motive_batch_import_and_clean <- import_and_clean_batch( import_list, import_method = "motive", import_messaging = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Each of these lists of objects can be bound into ## one viewr object (i.e. one tibble) via ## bind_viewr_objects() motive_bound_one <- bind_viewr_objects(motive_batch_cleaned) motive_bound_two <- bind_viewr_objects(motive_batch_import_and_clean) ## Either route results in the same object ultimately: identical(motive_bound_one, motive_bound_two)## Since we only have one example file of each type provided ## in pathviewr, we will simply import the same example multiple ## times to simulate batch importing. Replace the contents of ## the following list with your own list of files to be imported. ## Make a list of the same example file 3x import_list <- c(rep( system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), 3 )) ## Batch import motive_batch_imports <- import_batch(import_list, import_method = "motive", import_messaging = TRUE) ## Batch cleaning of these imported files ## via clean_viewr_batch() motive_batch_cleaned <- clean_viewr_batch( file_announce = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Alternatively, use import_and_clean_batch() to ## combine import with cleaning on a batch of files motive_batch_import_and_clean <- import_and_clean_batch( import_list, import_method = "motive", import_messaging = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Each of these lists of objects can be bound into ## one viewr object (i.e. one tibble) via ## bind_viewr_objects() motive_bound_one <- bind_viewr_objects(motive_batch_cleaned) motive_bound_two <- bind_viewr_objects(motive_batch_import_and_clean) ## Either route results in the same object ultimately: identical(motive_bound_one, motive_bound_two)
Calculate minimum distance to lateral and end walls in a box-shaped experimental tunnel
calc_min_dist_box(obj_name)calc_min_dist_box(obj_name)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
calc_min_dist_box() assumes the subject locomotes facing
forward, therefore min_dist_end represents the minimum distance
between the subject and the end wall to which it is moving towards.
All outputs are in meters.
A tibble or data.frame with added variables for
min_dist_pos, min_dist_neg, and min_dist_end,.
Eric R. Press
Other visual perception functions:
get_sf(),
get_vis_angle()
## Import sample data from package flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_sanders") ## Process data up to and including insert_treatments() flydra_data_full <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "box", tunnel_length = 3, tunnel_width = 1, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% ## Now calculate the minimum distances to each wall calc_min_dist_box() ## See 3 new variables for calculations to lateral and end walls names(flydra_data_full)## Import sample data from package flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_sanders") ## Process data up to and including insert_treatments() flydra_data_full <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "box", tunnel_length = 3, tunnel_width = 1, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% ## Now calculate the minimum distances to each wall calc_min_dist_box() ## See 3 new variables for calculations to lateral and end walls names(flydra_data_full)
Calculate minimum distance to lateral and end walls in a V-shaped experimental tunnel
calc_min_dist_v(obj_name, simplify_output = TRUE)calc_min_dist_v(obj_name, simplify_output = TRUE)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
simplify_output |
If TRUE, the returned object includes only the minimum distance between the subject and the lateral/end walls. If FALSE, the returned object includes all variables internal to the calculation. |
For tunnels in which vertex_angle is >90 degree,
bound_pos and bound_neg represent a planes orthogonal to the
lateral walls and are used to modify min_dist_pos and
min_dist_neg calculations to prevent erroneous outputs.
calc_min_dist_v() assumes the subject locomotes facing forward,
therefore min_dist_end represents the minimum distance between the
subject and the end wall to which it is moving towards All outputs are in
meters.
A tibble or data.frame with added variables for
height_2_vertex, height_2_screen, width_2_screen_pos,
width_2_screen_neg, min_dist_pos, min_dist_neg,
min_dist_end, bound_pos, and bound_neg.
Eric R. Press
Other mathematical functions:
deg_2_rad(),
find_curve_elbow(),
get_2d_angle(),
get_3d_angle(),
get_3d_cross_prod(),
get_dist_point_line(),
get_traj_velocities(),
get_velocity(),
rad_2_deg()
## Import sample data from package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Process data up to and including insert_treatments() motive_data_full <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "v", perch_2_vertex = 0.4, vertex_angle = 90, tunnel_length = 2, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% ## Now calculate the minimum distances to each wall calc_min_dist_v(simplify_output = TRUE) ## See 3 new variables for calculations to lateral and end walls names(motive_data_full)## Import sample data from package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Process data up to and including insert_treatments() motive_data_full <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "v", perch_2_vertex = 0.4, vertex_angle = 90, tunnel_length = 2, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% ## Now calculate the minimum distances to each wall calc_min_dist_v(simplify_output = TRUE) ## See 3 new variables for calculations to lateral and end walls names(motive_data_full)
Remove file_sub_traj entries that do not span the full region of interest
clean_by_span( obj_name, axis = "position_length", min_value = NULL, max_value = NULL, tolerance = 0.1 )clean_by_span( obj_name, axis = "position_length", min_value = NULL, max_value = NULL, tolerance = 0.1 )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
axis |
Along which axis should restrictions be enforced? |
min_value |
Minimum coordinate value; setting this to NULL will auto-compute the best value |
max_value |
Maximum coordinate; setting this to NULL will auto-compute the best value |
tolerance |
As a proporiton of axis value |
A viewr object (tibble or data.frame with attribute
pathviewr_steps. Trajectories that do not span the full region of
interest have been removed; trajectory identities (file_sub_traj) have
not been changed.
Vikram B. Baliga
Other utility functions:
insert_treatments(),
remove_duplicate_frames(),
remove_vel_anomalies(),
set_traj_frametime()
For an imported viewr object, run through the cleaning pipeline as desired
clean_viewr( obj_name, relabel_viewr_axes = TRUE, gather_tunnel_data = TRUE, trim_tunnel_outliers = TRUE, standardization_option = "rotate_tunnel", get_velocity = TRUE, select_x_percent = TRUE, rename_viewr_characters = FALSE, separate_trajectories = TRUE, get_full_trajectories = TRUE, fill_traj_gaps = FALSE, ... )clean_viewr( obj_name, relabel_viewr_axes = TRUE, gather_tunnel_data = TRUE, trim_tunnel_outliers = TRUE, standardization_option = "rotate_tunnel", get_velocity = TRUE, select_x_percent = TRUE, rename_viewr_characters = FALSE, separate_trajectories = TRUE, get_full_trajectories = TRUE, fill_traj_gaps = FALSE, ... )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
relabel_viewr_axes |
default TRUE, should axes be relabeled? |
gather_tunnel_data |
default TRUE, should tunnel data be gathered? |
trim_tunnel_outliers |
default TRUE, outliers be trimmed? |
standardization_option |
default "rotate_tunnel"; which standardization option should be used? See Details for more. |
get_velocity |
default TRUE, should velocity be computed? |
select_x_percent |
default TRUE, should a region of interest be selected? |
rename_viewr_characters |
default FALSE, should subjects be renamed? |
separate_trajectories |
default TRUE, should trajectories be defined? |
get_full_trajectories |
default TRUE, should only full trajectories be retained? |
fill_traj_gaps |
default FALSE, should gaps in trajectories be filled? |
... |
Additional arguments passed to any of the corresponding functions |
Each argument corresponds to a standalone function in
pathviewr. E.g. the parameter relabel_viewr_axes allows a
user to choose whether pathviewr::relabel_viewr_axes() is run
internally. Should the user desire to use any non-default parameter values
for any functions included here, they should be supplied to this function
as additional arguments formatted exactly as they would appear in their
corresponding function(s). E.g. if the "autodetect" feature in
pathviewr::separate_trajectories() is desired, add an argument
max_frame_gap = "autodetect" to the arguments supplied to this
function.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") that has passed
through several pathviewr functions as desired by the user,
resulting in data that have been cleaned and ready for analyses.
Vikram B. Baliga
Other all in one functions:
import_and_clean_viewr()
library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ## Alternatively, used the import_and_clean_viewr() ## function to combine these steps motive_import_and_clean <- import_and_clean_viewr( file_name = system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 )library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ## Alternatively, used the import_and_clean_viewr() ## function to combine these steps motive_import_and_clean <- import_and_clean_viewr( file_name = system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 )
For a list of viewr objects, run through the pipeline (from relabel axes up through get full trajectories, as desired) via clean_viewr()
clean_viewr_batch(obj_list, file_announce = FALSE, ...)clean_viewr_batch(obj_list, file_announce = FALSE, ...)
obj_list |
A list of viewr objects (i.e. a list of tibbles that each
have attribute |
file_announce |
Should the function report each time a file is processed? Default FALSE; if TRUE, a message will appear in the console each time a file has been cleaned successfully. |
... |
Arguments to be passed in that specify how this function should clean files. |
viewr objects should be in a list, e.g. the object generated by
import_batch().
See clean_viewr() for details of how cleaning steps are handled
and/or refer to the corresponding cleaning functions themselves.
A list of viewr objects (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") that have been passed
through the corresponding cleaning functions.
Vikram B. Baliga
Other batch functions:
bind_viewr_objects(),
import_and_clean_batch(),
import_batch()
## Since we only have one example file of each type provided ## in pathviewr, we will simply import the same example multiple ## times to simulate batch importing. Replace the contents of ## the following list with your own list of files to be imported. ## Make a list of the same example file 3x import_list <- c(rep( system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), 3 )) ## Batch import motive_batch_imports <- import_batch(import_list, import_method = "motive", import_messaging = TRUE) ## Batch cleaning of these imported files ## via clean_viewr_batch() motive_batch_cleaned <- clean_viewr_batch( file_announce = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Alternatively, use import_and_clean_batch() to ## combine import with cleaning on a batch of files motive_batch_import_and_clean <- import_and_clean_batch( import_list, import_method = "motive", import_messaging = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Each of these lists of objects can be bound into ## one viewr object (i.e. one tibble) via ## bind_viewr_objects() motive_bound_one <- bind_viewr_objects(motive_batch_cleaned) motive_bound_two <- bind_viewr_objects(motive_batch_import_and_clean) ## Either route results in the same object ultimately: identical(motive_bound_one, motive_bound_two)## Since we only have one example file of each type provided ## in pathviewr, we will simply import the same example multiple ## times to simulate batch importing. Replace the contents of ## the following list with your own list of files to be imported. ## Make a list of the same example file 3x import_list <- c(rep( system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), 3 )) ## Batch import motive_batch_imports <- import_batch(import_list, import_method = "motive", import_messaging = TRUE) ## Batch cleaning of these imported files ## via clean_viewr_batch() motive_batch_cleaned <- clean_viewr_batch( file_announce = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Alternatively, use import_and_clean_batch() to ## combine import with cleaning on a batch of files motive_batch_import_and_clean <- import_and_clean_batch( import_list, import_method = "motive", import_messaging = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Each of these lists of objects can be bound into ## one viewr object (i.e. one tibble) via ## bind_viewr_objects() motive_bound_one <- bind_viewr_objects(motive_batch_cleaned) motive_bound_two <- bind_viewr_objects(motive_batch_import_and_clean) ## Either route results in the same object ultimately: identical(motive_bound_one, motive_bound_two)
Convert degrees to radians
deg_2_rad(deg)deg_2_rad(deg)
deg |
Degrees (a numeric of any length >= 1) |
The angle(s) in radians (as a numeric vector of the same length)
Vikram B. Baliga
Other mathematical functions:
calc_min_dist_v(),
find_curve_elbow(),
get_2d_angle(),
get_3d_angle(),
get_3d_cross_prod(),
get_dist_point_line(),
get_traj_velocities(),
get_velocity(),
rad_2_deg()
## One input deg_2_rad(90) ## Multiple inputs deg_2_rad(c(5, 10, 15, 20))## One input deg_2_rad(90) ## Multiple inputs deg_2_rad(c(5, 10, 15, 20))
Remove trajectories from a viewr object that contain instances of velocity known to be spurious.
exclude_by_velocity(obj_name, vel_min = NULL, vel_max = NULL)exclude_by_velocity(obj_name, vel_min = NULL, vel_max = NULL)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
vel_min |
Default |
vel_max |
Default |
A new viewr object that is identical to the input object but now
excludes any trajectories that contain observations with velocity less than
vel_min (if specified) and/or velocity greater than vel_max
(if specified)
Vikram B. Baliga
## Import and clean the example Motive data motive_import_and_clean <- import_and_clean_viewr( file_name = system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## See the distribution of velocities hist(motive_import_and_clean$velocity) ## Let's remove any trajectories that contain ## velocity < 2 motive_vel_filtered <- motive_import_and_clean %>% exclude_by_velocity(vel_min = 2) ## See how the distribution of velocities has changed hist(motive_vel_filtered$velocity)## Import and clean the example Motive data motive_import_and_clean <- import_and_clean_viewr( file_name = system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## See the distribution of velocities hist(motive_import_and_clean$velocity) ## Let's remove any trajectories that contain ## velocity < 2 motive_vel_filtered <- motive_import_and_clean %>% exclude_by_velocity(vel_min = 2) ## See how the distribution of velocities has changed hist(motive_vel_filtered$velocity)
Use LOESS smoothing to fill in gaps of missing data within trajectories in a viewr object
fill_traj_gaps( obj_name, loess_degree = 1, loess_criterion = c("aicc", "gcv"), loess_family = c("gaussian", "symmetric"), loess_user_span = NULL )fill_traj_gaps( obj_name, loess_degree = 1, loess_criterion = c("aicc", "gcv"), loess_family = c("gaussian", "symmetric"), loess_user_span = NULL )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
loess_degree |
See "degree" argument of fANCOVA::loess.as() |
loess_criterion |
See "criterion" argument of fANCOVA::loess.as() |
loess_family |
See "family" argument of fANCOVA::loess.as() |
loess_user_span |
See "user.span" argument of fANCOVA::loess.as() |
It is strongly recommended that the input viewr object be "cleaned"
via select_x_percent() -> separate_trajectories() ->
get_full_trajectories() prior to using this function. Doing so will
ensure that only trajectories with minor gaps will be used in your
analyses. This function will then enable you to interpolate missing data in
those minor gaps.
Interpolation is handled by first fitting a series of LOESS regressions
(via fANCOVA::loess.as()). In each regression, a position axis (e.g.
position_length) is regressed against frame (frame is
x-axis). From that relationship, values of missing position data are
determined and then inserted into the original data set.
See loess.as for further details on parameters.
A viewr object; a tibble or data.frame with attribute
pathviewr_steps that includes "viewr" that now includes new
observations (rows) as a result of interpolation to fill in missing data. A
new column gaps_filled is added to the data to indicate original
data ("No") vs data that have been inserted to fill gaps ("Yes").
Vikram B. Baliga
library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean, isolate, and label trajectories motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ## Interpolate missing data via this function motive_filling <- motive_full %>% fill_traj_gaps() ## plot all trajectories (before) plot_viewr_trajectories(motive_full, multi_plot = TRUE) ## plot all trajectories(after) plot_viewr_trajectories(motive_filling, multi_plot = TRUE)library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean, isolate, and label trajectories motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ## Interpolate missing data via this function motive_filling <- motive_full %>% fill_traj_gaps() ## plot all trajectories (before) plot_viewr_trajectories(motive_full, multi_plot = TRUE) ## plot all trajectories(after) plot_viewr_trajectories(motive_filling, multi_plot = TRUE)
For bivariate data that show monotonic decreases (e.g. plots of trajectory count vs. frame gap allowed, or scree plots from PCAs), this function will find the "elbow" point. This is done by drawing an (imaginary) line between the first observation and the final observation. Then, the distance between that line and each observation is calculated. The "elbow" of the curve is the observation that maximizes this distance.
find_curve_elbow(data_frame, export_type = "row_num", plot_curve = FALSE)find_curve_elbow(data_frame, export_type = "row_num", plot_curve = FALSE)
data_frame |
A two-column data frame (numeric entries only), ordered x-axis first, y-axis second. |
export_type |
If "row_num" (the default), the row number of the elbow point is returned. If anything else, the entire row of the original data frame is returned. |
plot_curve |
Default FALSE; should the curve be plotted? |
If export_type is row_num the row number of the elbow
point is returned. If anything else is used for that argument, the entire
row of the original data frame on which the "elbow" is located is returned.
If plot_curve is TRUE, the curve is plotted along with a
vertical line drawn at the computed elbow point.
Vikram B. Baliga
Other mathematical functions:
calc_min_dist_v(),
deg_2_rad(),
get_2d_angle(),
get_3d_angle(),
get_3d_cross_prod(),
get_dist_point_line(),
get_traj_velocities(),
get_velocity(),
rad_2_deg()
df <- data.frame(x = seq(1:10), y = 1/seq(1:10)) plot(df) find_curve_elbow(df, plot_curve = TRUE)df <- data.frame(x = seq(1:10), y = 1/seq(1:10)) plot(df) find_curve_elbow(df, plot_curve = TRUE)
Reformat viewr data into a "tidy" format so that every row corresponds
to the position (and potentially rotation) of a single subject during an
observed frame and time.
gather_tunnel_data(obj_name, NA_drop = TRUE, ...)gather_tunnel_data(obj_name, NA_drop = TRUE, ...)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
NA_drop |
Should rows with NAs be dropped? Defaults to |
... |
Additional arguments that can be passed to other |
The tibble or data.frame that is fed in must have variables that
have subject names and axis names separated by underscores. Axis names must
be one of the following: position_length, position_width, or
position_height. Each of these three dimensions must be present in the
data. Collectively, this means that names like bird01_position_length
or larry_position_height are acceptable, but bird01_x or
bird01_length are not.
A tibble in "tidy" format which is formatted to have every row
correspond to the position (and potentially rotation) of a single subject
during an observed frame and time. Subjects' names are automatically parsed
from original variable names (e.g. subject1_rotation_width extracts
"subject1" as the subject name) and stored in a Subjects column in the
returned tibble.
Vikram B. Baliga
Other data cleaning functions:
get_full_trajectories(),
quick_separate_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rename_viewr_characters(),
rotate_tunnel(),
select_x_percent(),
separate_trajectories(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
library(pathviewr) ## Import the Motive example data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## First use relabel_viewr_axes() to rename these variables using _length, ## _width, and _height instead motive_data_relabeled <- relabel_viewr_axes(motive_data) ## Now use gather_tunnel_data() to gather colums into tidy format motive_data_gathered <- gather_tunnel_data(motive_data_relabeled) ## Column names reflect the way in which data were reformatted: names(motive_data_gathered)library(pathviewr) ## Import the Motive example data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## First use relabel_viewr_axes() to rename these variables using _length, ## _width, and _height instead motive_data_relabeled <- relabel_viewr_axes(motive_data) ## Now use gather_tunnel_data() to gather colums into tidy format motive_data_gathered <- gather_tunnel_data(motive_data_relabeled) ## Column names reflect the way in which data were reformatted: names(motive_data_gathered)
Compute an angle in 2D space
get_2d_angle(x1, y1, x2, y2, x3, y3)get_2d_angle(x1, y1, x2, y2, x3, y3)
x1 |
x-coordinate of first point |
y1 |
y-coordinate of first point |
x2 |
x-coordinate of second point (vertex) |
y2 |
y-coordinate of second point (vertex) |
x3 |
x-coordinate of third point |
y3 |
y-coordinate of third point |
Everything supplied to arguments must be numeric values or vectors of numeric values. The second point (x2, y2) is treated as the vertex, and the angle between the three points in 2D space is computed.
A numeric vector that provides the angular measurement in degrees.
Vikram B. Baliga
Other mathematical functions:
calc_min_dist_v(),
deg_2_rad(),
find_curve_elbow(),
get_3d_angle(),
get_3d_cross_prod(),
get_dist_point_line(),
get_traj_velocities(),
get_velocity(),
rad_2_deg()
get_2d_angle( 0, 1, 0, 0, 1, 0)get_2d_angle( 0, 1, 0, 0, 1, 0)
Compute an angle in 3D space
get_3d_angle(x1, y1, z1, x2, y2, z2, x3, y3, z3)get_3d_angle(x1, y1, z1, x2, y2, z2, x3, y3, z3)
x1 |
x-coordinate of first point |
y1 |
y-coordinate of first point |
z1 |
z-coordinate of first point |
x2 |
x-coordinate of second point (vertex) |
y2 |
y-coordinate of second point (vertex) |
z2 |
y-coordinate of second point (vertex) |
x3 |
x-coordinate of third point |
y3 |
y-coordinate of third point |
z3 |
z-coordinate of third point |
Everything supplied to arguments must be numeric values or vectors of numeric values. The second point (x2, y2, z2) is treated as the vertex, and the angle between the three points in 3D space is computed.
A numeric vector that provides the angular measurement in degrees.
Vikram B. Baliga
Other mathematical functions:
calc_min_dist_v(),
deg_2_rad(),
find_curve_elbow(),
get_2d_angle(),
get_3d_cross_prod(),
get_dist_point_line(),
get_traj_velocities(),
get_velocity(),
rad_2_deg()
get_3d_angle( 0, 1, 0, 0, 0, 0, 1, 0, 0)get_3d_angle( 0, 1, 0, 0, 0, 0, 1, 0, 0)
Compute the cross product of two 3D vectors
get_3d_cross_prod(v1, v2)get_3d_cross_prod(v1, v2)
v1 |
First vector, as c(x,y,z) |
v2 |
Second vector, as c(x,y,z) |
A vector of length 3 that describes the cross-product
Vikram B. Baliga
Other mathematical functions:
calc_min_dist_v(),
deg_2_rad(),
find_curve_elbow(),
get_2d_angle(),
get_3d_angle(),
get_dist_point_line(),
get_traj_velocities(),
get_velocity(),
rad_2_deg()
v1 <- c(1, 1, 3) v2 <- c(3, 1, 3) get_3d_cross_prod(v1, v2)v1 <- c(1, 1, 3) v2 <- c(3, 1, 3) get_3d_cross_prod(v1, v2)
Compute distance between a point and a line
get_dist_point_line(point, line_coord1, line_coord2)get_dist_point_line(point, line_coord1, line_coord2)
point |
2D or 3D coordinates of the point as c(x,y) or c(x,y,z) |
line_coord1 |
2D or 3D coordinates of one point on the line as c(x,y) or c(x,y,z) |
line_coord2 |
2D or 3D coordinates of a second point on the line as c(x,y) or c(x,y,z) |
The function accepts 2D coordinates or 3D coordinates, but note that the dimensions of all supplied arguments must match; all coordinates must be 2D or they all must be 3D.
A numeric vector of length 1 that provides the euclidean distance between the point and the line.
Vikram B. Baliga
Other mathematical functions:
calc_min_dist_v(),
deg_2_rad(),
find_curve_elbow(),
get_2d_angle(),
get_3d_angle(),
get_3d_cross_prod(),
get_traj_velocities(),
get_velocity(),
rad_2_deg()
## 2D case get_dist_point_line( point = c(0, 0), line_coord1 = c(1, 0), line_coord2 = c(1, 5) ) ## 3D case get_dist_point_line( point = c(0, 0, 0), line_coord1 = c(1, 0, 0), line_coord2 = c(1, 5, 0) )## 2D case get_dist_point_line( point = c(0, 0), line_coord1 = c(1, 0), line_coord2 = c(1, 5) ) ## 3D case get_dist_point_line( point = c(0, 0, 0), line_coord1 = c(1, 0, 0), line_coord2 = c(1, 5, 0) )
Specify a minimum span of the selected region of interest and then keep trajectories that are wider than that span and go from one end to the other of the region.
get_full_trajectories(obj_name, span = 0.8, ...)get_full_trajectories(obj_name, span = 0.8, ...)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
span |
Span to use; must be numeric and between 0 and 1 |
... |
Additional arguments passed to/from other pathviewr functions |
Because trajectories may not have observations exactly at the
beginning or the end of the region of interest, it may be necessary to allow
trajectories to be slightly shorter than the range of the selected region of
interest. The span parameter of this function handles this. By
supplying a numeric proportion from 0 to 1, a user may allow trajectories to
span that proportion of the selected region. For example, setting span
= 0.95 will keep all trajectories that span 95% of the length of the
selected region of interest. Setting span = 1 (not recommended) will
strictly keep trajectories that start and end at the exact cut-offs of the
selected region of interest. For these reasons, spans of 0.99 to 0.95
are generally recommended.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") in which only
trajectories that span the region of interest are retained. Data are
labeled by direction (either "leftwards" or "rightwards") with respect to
their starting and ending position_length values in the
direction column.
Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
quick_separate_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rename_viewr_characters(),
rotate_tunnel(),
select_x_percent(),
separate_trajectories(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
Other functions that define or clean trajectories:
quick_separate_trajectories(),
separate_trajectories(),
visualize_frame_gap_choice()
motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "separate" step before running select_x_percent(). motive_separated <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect", frame_rate_proportion = 0.1) ## Now retain only the "full" trajectories that span ## across 0.95 of the range of position_length motive_full <- motive_separated %>% get_full_trajectories(span = 0.95)motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "separate" step before running select_x_percent(). motive_separated <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect", frame_rate_proportion = 0.1) ## Now retain only the "full" trajectories that span ## across 0.95 of the range of position_length motive_full <- motive_separated %>% get_full_trajectories(span = 0.95)
A function to quickly return the information stored in the header of the
original data file imported via pathviewr's read_ functions.
get_header_viewr(obj_name, ...)get_header_viewr(obj_name, ...)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
... |
Additional arguments that may be passed to other |
The value of the header attribute, or NULL if no exact match
is found and no or more than one partial match is found.
Vikram B. Baliga
library(pathviewr) ## Import the Motive example data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Now display the Header information get_header_viewr(motive_data)library(pathviewr) ## Import the Motive example data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Now display the Header information get_header_viewr(motive_data)
Estimate the spatial frequency of visual stimuli from the subject's perspective in an experimental tunnel.
get_sf(obj_name)get_sf(obj_name)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
get_sf() assumes the following:
The subject's gaze is fixed at the point on the either side of the tunnel that minimizes the distance to visual stimuli and therefore maximizes visual angles.
The subject's head is facing parallel to the length axis of the tunnel. Visual perception functions in future versions of pathviewr will integrate head orientation coordinates. Spatial frequency is reported in cycles/degree and is the inverse of visual angle (degrees/cycle).
A tibble or data.frame with added variables for
sf_pos, sf_neg, and sf_end.
angle.
Eric R. Press
Other visual perception functions:
calc_min_dist_box(),
get_vis_angle()
## Import sample data from package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_sanders") ## Process data up to and including get_vis_angle() motive_data_full <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "v", perch_2_vertex = 0.4, vertex_angle = 90, tunnel_length = 2, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% calc_min_dist_v(simplify_output = TRUE) %>% get_vis_angle() %>% ## Now calculate the spatial frequencies get_sf() flydra_data_full <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "box", tunnel_length = 3, tunnel_width = 1, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% calc_min_dist_box() %>% get_vis_angle() %>% ## Now calculate the spatial frequencies get_sf()## Import sample data from package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_sanders") ## Process data up to and including get_vis_angle() motive_data_full <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "v", perch_2_vertex = 0.4, vertex_angle = 90, tunnel_length = 2, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% calc_min_dist_v(simplify_output = TRUE) %>% get_vis_angle() %>% ## Now calculate the spatial frequencies get_sf() flydra_data_full <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "box", tunnel_length = 3, tunnel_width = 1, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% calc_min_dist_box() %>% get_vis_angle() %>% ## Now calculate the spatial frequencies get_sf()
Recompute trajectory-specific velocities
get_traj_velocities( obj_name, time_col = "time_sec", length_col = "position_length", width_col = "position_width", height_col = "position_height", set_init_vel_zero = FALSE, velocity_min = NA, velocity_max = NA )get_traj_velocities( obj_name, time_col = "time_sec", length_col = "position_length", width_col = "position_width", height_col = "position_height", set_init_vel_zero = FALSE, velocity_min = NA, velocity_max = NA )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
time_col |
Name of the column containing time |
length_col |
Name of the column containing length dimension |
width_col |
Name of the column containing width dimension |
height_col |
Name of the column containing height dimension |
set_init_vel_zero |
Should the first value be zero or can it be a duplicate of the second velocity value? Defaults to FALSE. |
velocity_min |
Should data below a certain velocity be filtered out of the object? If so, enter a numeric. If not, keep NA. |
velocity_max |
Should data above a certain velocity be filtered out of the object? If so, enter a numeric. If not, keep NA. |
Instantaneous velocity is not truly "instantaneous" but rather is approximated as the change in distance divided by change in time from one observation (row) to the previous observation (row). Each component of velocity is computed (i.e. per axis) along with the overall velocity of the subject.
If add_to_viewr is TRUE, additional columns are
appended to the input viewr object. If FALSE, a standalone tibble is
created. Either way, an "instantaneous" velocity is computed as the
difference in position divided by the difference in time as each successive
row is encountered. Additionally, velocities along each of the three
position axes are computed and provided as additional columns.
Vikram B. Baliga
Other mathematical functions:
calc_min_dist_v(),
deg_2_rad(),
find_curve_elbow(),
get_2d_angle(),
get_3d_angle(),
get_3d_cross_prod(),
get_dist_point_line(),
get_velocity(),
rad_2_deg()
Velocity (both overall and per-axis) is computed for each row in the data (see Details)
get_velocity( obj_name, time_col = "time_sec", length_col = "position_length", width_col = "position_width", height_col = "position_height", add_to_viewr = TRUE, velocity_min = NA, velocity_max = NA, ... )get_velocity( obj_name, time_col = "time_sec", length_col = "position_length", width_col = "position_width", height_col = "position_height", add_to_viewr = TRUE, velocity_min = NA, velocity_max = NA, ... )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
time_col |
Name of the column containing time |
length_col |
Name of the column containing length dimension |
width_col |
Name of the column containing width dimension |
height_col |
Name of the column containing height dimension |
add_to_viewr |
Default TRUE; should velocity data be added as new columns or should this function create a new simpler object? |
velocity_min |
Should data below a certain velocity be filtered out of the object? If so, enter a numeric. If not, keep NA. |
velocity_max |
Should data above a certain velocity be filtered out of the object? If so, enter a numeric. If not, keep NA. |
... |
Additional arguments passed to or from other pathviewr functions. |
Instantaneous velocity is not truly "instantaneous" but rather is approximated as the change in distance divided by change in time from one observation (row) to the previous observation (row). Each component of velocity is computed (i.e. per axis) along with the overall velocity of the subject.
If add_to_viewr is TRUE, additional columns are
appended to the input viewr object. If FALSE, a standalone tibble is
created. Either way, an "instantaneous" velocity is computed as the
difference in position divided by the difference in time as each successive
row is encountered. Additionally, velocities along each of the three
position axes are computed and provided as additional columns.
Vikram B. Baliga and Melissa S. Armstrong
Other mathematical functions:
calc_min_dist_v(),
deg_2_rad(),
find_curve_elbow(),
get_2d_angle(),
get_3d_angle(),
get_3d_cross_prod(),
get_dist_point_line(),
get_traj_velocities(),
rad_2_deg()
## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "standarization" step before running get_velocity(). motive_cleaned <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() ## Now compute velocity and add as columns motive_velocity_added <- motive_cleaned %>% get_velocity(add_to_viewr = TRUE) ## Or set add_to_viewr to FALSE for a standalone object motive_velocity_standalone <- motive_cleaned %>% get_velocity(add_to_viewr = TRUE)## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "standarization" step before running get_velocity(). motive_cleaned <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() ## Now compute velocity and add as columns motive_velocity_added <- motive_cleaned %>% get_velocity(add_to_viewr = TRUE) ## Or set add_to_viewr to FALSE for a standalone object motive_velocity_standalone <- motive_cleaned %>% get_velocity(add_to_viewr = TRUE)
Estimate visual angles from a subject's perspective in an experimental tunnel
get_vis_angle(obj_name)get_vis_angle(obj_name)
obj_name |
The input viewr object; a tibble or data.frame with
attributes |
get_vis_angle() assumes the following:
The subject's gaze is fixed at the point on the either side of the tunnel that minimizes the distance to visual stimuli and therefore maximizes visual angles.
The subject's head is facing parallel to the length axis of the tunnel.
Visual perception functions in future versions of pathviewr will integrate
head orientation coordinates.
Angles are reported in radians/cycle (vis_angle_pos_rad) and
degrees/cycle (vis_angle_pos_deg).
A tibble or data.frame with added variables for
vis_angle_pos_rad, vis_angle_pos_deg,
vis_angle_neg_rad, vos_angle_neg_deg,
vis_angle_end_rad, and vis_angle_end_deg.
Eric R. Press
Other visual perception functions:
calc_min_dist_box(),
get_sf()
## Import sample data from package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_sanders") ## Process data up to and including get_min_dist() motive_data_full <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "v", perch_2_vertex = 0.4, vertex_angle = 90, tunnel_length = 2, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% calc_min_dist_v(simplify_output = TRUE) %>% ## Now calculate the visual angles get_vis_angle() flydra_data_full <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "box", tunnel_length = 3, tunnel_width = 1, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% calc_min_dist_box() %>% ## Now calculate the visual angles get_vis_angle()## Import sample data from package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_sanders") ## Process data up to and including get_min_dist() motive_data_full <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "v", perch_2_vertex = 0.4, vertex_angle = 90, tunnel_length = 2, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% calc_min_dist_v(simplify_output = TRUE) %>% ## Now calculate the visual angles get_vis_angle() flydra_data_full <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) %>% insert_treatments(tunnel_config = "box", tunnel_length = 3, tunnel_width = 1, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") %>% calc_min_dist_box() %>% ## Now calculate the visual angles get_vis_angle()
Like clean_viewr_batch(), but with import as the first step too
import_and_clean_batch( file_path_list, import_method = c("flydra", "motive"), file_id = NA, subject_name = NULL, frame_rate = NULL, simplify_marker_naming = TRUE, import_messaging = FALSE, ... )import_and_clean_batch( file_path_list, import_method = c("flydra", "motive"), file_id = NA, subject_name = NULL, frame_rate = NULL, simplify_marker_naming = TRUE, import_messaging = FALSE, ... )
file_path_list |
A list of file paths leading to files to be imported. |
import_method |
Either "flydra" or "motive" |
file_id |
(Optional) identifier for this file. If not supplied, this
defaults to |
subject_name |
For Flydra, the subject name applied to all files |
frame_rate |
For Flydra, the frame rate applied to all files |
simplify_marker_naming |
For Motive, if Markers are encountered, should they be renamed from "Subject:marker" to "marker"? Defaults to TRUE |
import_messaging |
Should this function report each time a file has been processed? |
... |
Additional arguments to specify how data should be cleaned. |
viewr objects should be in a list, e.g. the object generated by
import_batch().
See clean_viewr() for details of how cleaning steps are handled
and/or refer to the corresponding cleaning functions themselves.
A list of viewr objects (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") that have been passed
through the corresponding cleaning functions.
Vikram B. Baliga
Other data import functions:
as_viewr(),
import_batch(),
read_flydra_mat(),
read_motive_csv()
Other batch functions:
bind_viewr_objects(),
clean_viewr_batch(),
import_batch()
## Since we only have one example file of each type provided ## in pathviewr, we will simply import the same example multiple ## times to simulate batch importing. Replace the contents of ## the following list with your own list of files to be imported. ## Make a list of the same example file 3x import_list <- c(rep( system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), 3 )) ## Batch import motive_batch_imports <- import_batch(import_list, import_method = "motive", import_messaging = TRUE) ## Batch cleaning of these imported files ## via clean_viewr_batch() motive_batch_cleaned <- clean_viewr_batch( file_announce = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Alternatively, use import_and_clean_batch() to ## combine import with cleaning on a batch of files motive_batch_import_and_clean <- import_and_clean_batch( import_list, import_method = "motive", import_messaging = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Each of these lists of objects can be bound into ## one viewr object (i.e. one tibble) via ## bind_viewr_objects() motive_bound_one <- bind_viewr_objects(motive_batch_cleaned) motive_bound_two <- bind_viewr_objects(motive_batch_import_and_clean) ## Either route results in the same object ultimately: identical(motive_bound_one, motive_bound_two)## Since we only have one example file of each type provided ## in pathviewr, we will simply import the same example multiple ## times to simulate batch importing. Replace the contents of ## the following list with your own list of files to be imported. ## Make a list of the same example file 3x import_list <- c(rep( system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), 3 )) ## Batch import motive_batch_imports <- import_batch(import_list, import_method = "motive", import_messaging = TRUE) ## Batch cleaning of these imported files ## via clean_viewr_batch() motive_batch_cleaned <- clean_viewr_batch( file_announce = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Alternatively, use import_and_clean_batch() to ## combine import with cleaning on a batch of files motive_batch_import_and_clean <- import_and_clean_batch( import_list, import_method = "motive", import_messaging = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Each of these lists of objects can be bound into ## one viewr object (i.e. one tibble) via ## bind_viewr_objects() motive_bound_one <- bind_viewr_objects(motive_batch_cleaned) motive_bound_two <- bind_viewr_objects(motive_batch_import_and_clean) ## Either route results in the same object ultimately: identical(motive_bound_one, motive_bound_two)
Import a file and then, akin to clean_viewr, run through as many
cleaning steps as desired.
import_and_clean_viewr( file_name, file_id = NA, relabel_viewr_axes = TRUE, gather_tunnel_data = TRUE, trim_tunnel_outliers = TRUE, standardization_option = "rotate_tunnel", get_velocity = TRUE, select_x_percent = TRUE, rename_viewr_characters = FALSE, separate_trajectories = TRUE, get_full_trajectories = TRUE, fill_traj_gaps = FALSE, ... )import_and_clean_viewr( file_name, file_id = NA, relabel_viewr_axes = TRUE, gather_tunnel_data = TRUE, trim_tunnel_outliers = TRUE, standardization_option = "rotate_tunnel", get_velocity = TRUE, select_x_percent = TRUE, rename_viewr_characters = FALSE, separate_trajectories = TRUE, get_full_trajectories = TRUE, fill_traj_gaps = FALSE, ... )
file_name |
Target file |
file_id |
Optional |
relabel_viewr_axes |
default TRUE, should axes be relabeled? |
gather_tunnel_data |
default TRUE, should tunnel data be gathered? |
trim_tunnel_outliers |
default TRUE, outliers be trimmed? |
standardization_option |
default "rotate_tunnel"; which standardization option should be used? See Details for more. |
get_velocity |
default TRUE, should velocity be computed? |
select_x_percent |
default TRUE, should a region of interest be selected? |
rename_viewr_characters |
default FALSE, should subjects be renamed? |
separate_trajectories |
default TRUE, should trajectories be defined? |
get_full_trajectories |
default TRUE, should only full trajectories be retained? |
fill_traj_gaps |
default FALSE, should gaps in trajectories be filled? |
... |
Additional arguments passed to the corresponding functions. |
Each argument corresponds to a standalone function in
pathviewr. E.g. the parameter relabel_viewr_axes allows a
user to choose whether pathviewr::relabel_viewr_axes() is run
internally. Should the user desire to use any non-default parameter values
for any functions included here, they should be supplied to this function
as additional arguments formatted exactly as they would appear in their
corresponding function(s). E.g. if the "autodetect" feature in
pathviewr::separate_trajectories() is desired, add an argument
max_frame_gap = "autodetect" to the arguments supplied to this
function.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") that has passed
through several pathviewr functions as desired by the user,
resulting in data that have been cleaned and ready for analyses.
Vikram B. Baliga
Other all in one functions:
clean_viewr()
library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ## Alternatively, used the import_and_clean_viewr() ## function to combine these steps motive_import_and_clean <- import_and_clean_viewr( file_name = system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 )library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ## Alternatively, used the import_and_clean_viewr() ## function to combine these steps motive_import_and_clean <- import_and_clean_viewr( file_name = system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 )
Batch import of files for either Motive or Flydra (but not a mix of both)
import_batch( file_path_list, import_method = c("flydra", "motive"), file_id = NA, subject_name = NULL, frame_rate = NULL, simplify_marker_naming = TRUE, import_messaging = FALSE, ... )import_batch( file_path_list, import_method = c("flydra", "motive"), file_id = NA, subject_name = NULL, frame_rate = NULL, simplify_marker_naming = TRUE, import_messaging = FALSE, ... )
file_path_list |
A list of file paths |
import_method |
Either "flydra" or "motive" |
file_id |
Optional |
subject_name |
For Flydra, the assigned subject name |
frame_rate |
For Flydra, the assigned frame rate |
simplify_marker_naming |
default TRUE; for Motive, whether marker naming should be simplified |
import_messaging |
default FALSE; should this function report each time a file has been imported? |
... |
Additional arguments (may remove this if needed) |
Refer to read_motive_csv() and read_flydra_mat() for
details of data import methods.
A list of viewr objects (tibble or data.frame with attribute
pathviewr_steps that includes "viewr").
Vikram B. Baliga
Other data import functions:
as_viewr(),
import_and_clean_batch(),
read_flydra_mat(),
read_motive_csv()
Other batch functions:
bind_viewr_objects(),
clean_viewr_batch(),
import_and_clean_batch()
## Since we only have one example file of each type provided ## in pathviewr, we will simply import the same example multiple ## times to simulate batch importing. Replace the contents of ## the following list with your own list of files to be imported. ## Make a list of the same example file 3x import_list <- c(rep( system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), 3 )) ## Batch import motive_batch_imports <- import_batch(import_list, import_method = "motive", import_messaging = TRUE) ## Batch cleaning of these imported files ## via clean_viewr_batch() motive_batch_cleaned <- clean_viewr_batch( file_announce = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Alternatively, use import_and_clean_batch() to ## combine import with cleaning on a batch of files motive_batch_import_and_clean <- import_and_clean_batch( import_list, import_method = "motive", import_messaging = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Each of these lists of objects can be bound into ## one viewr object (i.e. one tibble) via ## bind_viewr_objects() motive_bound_one <- bind_viewr_objects(motive_batch_cleaned) motive_bound_two <- bind_viewr_objects(motive_batch_import_and_clean) ## Either route results in the same object ultimately: identical(motive_bound_one, motive_bound_two)## Since we only have one example file of each type provided ## in pathviewr, we will simply import the same example multiple ## times to simulate batch importing. Replace the contents of ## the following list with your own list of files to be imported. ## Make a list of the same example file 3x import_list <- c(rep( system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr'), 3 )) ## Batch import motive_batch_imports <- import_batch(import_list, import_method = "motive", import_messaging = TRUE) ## Batch cleaning of these imported files ## via clean_viewr_batch() motive_batch_cleaned <- clean_viewr_batch( file_announce = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Alternatively, use import_and_clean_batch() to ## combine import with cleaning on a batch of files motive_batch_import_and_clean <- import_and_clean_batch( import_list, import_method = "motive", import_messaging = TRUE, motive_batch_imports, desired_percent = 50, max_frame_gap = "autodetect", span = 0.95 ) ## Each of these lists of objects can be bound into ## one viewr object (i.e. one tibble) via ## bind_viewr_objects() motive_bound_one <- bind_viewr_objects(motive_batch_cleaned) motive_bound_two <- bind_viewr_objects(motive_batch_import_and_clean) ## Either route results in the same object ultimately: identical(motive_bound_one, motive_bound_two)
Adds information about treatment and experimental set up to viewr objects for analysis in other pathviewr functions
insert_treatments( obj_name, tunnel_config = "box", perch_2_vertex = NULL, vertex_angle = NULL, tunnel_width = NULL, tunnel_length = NULL, stim_param_lat_pos = NULL, stim_param_lat_neg = NULL, stim_param_end_pos = NULL, stim_param_end_neg = NULL, treatment = NULL )insert_treatments( obj_name, tunnel_config = "box", perch_2_vertex = NULL, vertex_angle = NULL, tunnel_width = NULL, tunnel_length = NULL, stim_param_lat_pos = NULL, stim_param_lat_neg = NULL, stim_param_end_pos = NULL, stim_param_end_neg = NULL, treatment = NULL )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
tunnel_config |
The configuration of the experimental tunnel. Currently, pathviewr supports rectangular "box" and V-shaped tunnel configurations. |
perch_2_vertex |
If using a V-shaped tunnel, this is the vertical distance between the vertex and the height of the perches. If the tunnel does not have perches, insert the vertical distance between the vertex and the height of the origin (0,0,0). |
vertex_angle |
If using a V-shaped tunnel, the angle of the vertex (in
degrees) |
tunnel_width |
If using a box-shaped tunnel, the width of the tunnel. |
tunnel_length |
The length of the tunnel. |
stim_param_lat_pos |
The size of the stimulus on the lateral positive
wall of the tunnel. Eg. for 10cm wide gratings,
|
stim_param_lat_neg |
The size of the stimulus on the lateral negative wall of the tunnel.. |
stim_param_end_pos |
The size of the stimulus on the end positive wall of the tunnel. |
stim_param_end_neg |
The size of the stimulus on the end negative wall of the tunnel. |
treatment |
The name of the treatment assigned to all rows of the viewr object. Currently only able to accept a single treatment per viewr data object. |
All length measurements reported in meters.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "treatments added"). Depending
on the argument tunnel_config, the viewr object also includes
columns storing the values of the supplied arguments. This experimental
information is also stored in the viewr object's metadata
Eric R. Press
Other utility functions:
clean_by_span(),
remove_duplicate_frames(),
remove_vel_anomalies(),
set_traj_frametime()
## Import sample data from package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_sanders") ## Clean data up to and including get_full_trajectories() motive_data_full <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) flydra_data_full <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) ## Now add information about the experimental configuration. In this example, ## a V-shaped tunnel in which the vertex is 90deg and lies 0.40m below the ## origin. The visual stimuli on the lateral and end walls have a cycle ## length of 0.1m and 0.3m respectively, and the treatment is labeled ## "lat10_end30" motive_v <- motive_data_full %>% insert_treatments(tunnel_config = "v", perch_2_vertex = 0.4, vertex_angle = 90, tunnel_length = 2, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") # For an experiment using the box-shaped configuration where the tunnel is 1m # wide and 3m long and the visual stimuli on the lateral and end walls have a # cycle length of 0.2 and 0.3m, respectively, and the treatment is labeled # "lat20_end30". flydra_box <- flydra_data_full %>% insert_treatments(tunnel_config = "box", tunnel_width = 1, tunnel_length = 3, stim_param_lat_pos = 0.2, stim_param_lat_neg = 0.2, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat20_end30") ## Check out the new columns in the resulting objects names(motive_v) names(flydra_box)## Import sample data from package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_sanders") ## Clean data up to and including get_full_trajectories() motive_data_full <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) flydra_data_full <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = "autodetect") %>% get_full_trajectories(span = 0.95) ## Now add information about the experimental configuration. In this example, ## a V-shaped tunnel in which the vertex is 90deg and lies 0.40m below the ## origin. The visual stimuli on the lateral and end walls have a cycle ## length of 0.1m and 0.3m respectively, and the treatment is labeled ## "lat10_end30" motive_v <- motive_data_full %>% insert_treatments(tunnel_config = "v", perch_2_vertex = 0.4, vertex_angle = 90, tunnel_length = 2, stim_param_lat_pos = 0.1, stim_param_lat_neg = 0.1, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat10_end_30") # For an experiment using the box-shaped configuration where the tunnel is 1m # wide and 3m long and the visual stimuli on the lateral and end walls have a # cycle length of 0.2 and 0.3m, respectively, and the treatment is labeled # "lat20_end30". flydra_box <- flydra_data_full %>% insert_treatments(tunnel_config = "box", tunnel_width = 1, tunnel_length = 3, stim_param_lat_pos = 0.2, stim_param_lat_neg = 0.2, stim_param_end_pos = 0.3, stim_param_end_neg = 0.3, treatment = "lat20_end30") ## Check out the new columns in the resulting objects names(motive_v) names(flydra_box)
Plots all trajectories and generates density plots of position by subject from elevation and bird's eye views.
plot_by_subject(obj_name, col_by_treat = FALSE, ...)plot_by_subject(obj_name, col_by_treat = FALSE, ...)
obj_name |
A viewr object (a tibble or data.frame with attribute
|
col_by_treat |
If multiple treatments or sessions, color data per
treatment or session. Treatments must be levels in a column named
|
... |
Additional arguments passed to/from other pathviewr functions. |
The input viewr object should have passed through
separate_trajectories() or get_full_trajectories().
Optionally, treatments should have been added as levels in a column named
treatment. Two plots will be produced, one from a "bird's eye view"
of width against length and one from an "elevation view" of height against
length. All trajectories will be plotted on a per subject basis, along with
density plots of width or height depending on the view.
col_by_treat = TRUE, data will be plotted by color according to
treatment in both the trajectory plots and the density plots.
A "bird's eye view" plot and an "elevation view" plot, made via ggplot2.
Melissa S. Armstrong
Other plotting functions:
plot_viewr_trajectories(),
visualize_frame_gap_choice()
library(pathviewr) library(ggplot2) library(magrittr) if (interactive()) { ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean, isolate, and label trajectories motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ## Plot all trajectories by subject motive_full %>% plot_by_subject() ## Add treatment information motive_full$treatment <- c(rep("latA", 100), rep("latB", 100), rep("latA", 100), rep("latB", 149)) ## Plot all trajectories by subject, color by treatment motive_full %>% plot_by_subject(col_by_treat = TRUE) }library(pathviewr) library(ggplot2) library(magrittr) if (interactive()) { ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean, isolate, and label trajectories motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ## Plot all trajectories by subject motive_full %>% plot_by_subject() ## Add treatment information motive_full$treatment <- c(rep("latA", 100), rep("latB", 100), rep("latA", 100), rep("latB", 149)) ## Plot all trajectories by subject, color by treatment motive_full %>% plot_by_subject(col_by_treat = TRUE) }
Plot each trajectory within a viewr object
plot_viewr_trajectories( obj_name, plot_axes = c("length", "width"), multi_plot = FALSE )plot_viewr_trajectories( obj_name, plot_axes = c("length", "width"), multi_plot = FALSE )
obj_name |
A viewr object (a tibble or data.frame with attribute
|
plot_axes |
Which position axes should be plotted? A character vector
including exactly two of the following choices must be supplied:
|
multi_plot |
Should separate plots (one per trajectory) be created or should one multi-plot grid be generated. Defaults to FALSE, which produces separate plots. |
A (base-R) series of plots or single plot (if multi_plot =
TRUE) that depict each trajectory along the chosen axes.
Vikram B. Baliga
Other plotting functions:
plot_by_subject(),
visualize_frame_gap_choice()
library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) plot_viewr_trajectories(motive_full, multi_plot = FALSE) plot_viewr_trajectories(motive_full, multi_plot = TRUE)library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) plot_viewr_trajectories(motive_full, multi_plot = FALSE) plot_viewr_trajectories(motive_full, multi_plot = TRUE)
Mostly meant for internal use but available nevertheless.
quick_separate_trajectories(obj_name, max_frame_gap = 1, ...)quick_separate_trajectories(obj_name, max_frame_gap = 1, ...)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
max_frame_gap |
Unlike the corresponding parameter in
|
... |
Additional arguments passed to/from other pathviewr functions |
This function is designed to separate rows of data into separately labeled trajectories.
The max_frame_gap parameter determines how trajectories will be
separated. max_frame_gap defines the largest permissible gap in data
before a new trajectory is forced to be defined. In this function, only a
single numeric can be supplied to this parameter (unlike the case in
separate_trajectories).
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") in which a new column
file_sub_traj is added, which labels trajectories within the data by
concatenating file name, subject name, and a trajectory number (all
separated by underscores).
Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rename_viewr_characters(),
rotate_tunnel(),
select_x_percent(),
separate_trajectories(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
Other functions that define or clean trajectories:
get_full_trajectories(),
separate_trajectories(),
visualize_frame_gap_choice()
## This function is not recommended for general use. ## See separate_trajectories() instead## This function is not recommended for general use. ## See separate_trajectories() instead
Convert radians to degrees
rad_2_deg(rad)rad_2_deg(rad)
rad |
Radians (a numeric of any length >= 1) |
The angle(s) in degrees (as a numeric vector of the same length)
Vikram B. Baliga
Other mathematical functions:
calc_min_dist_v(),
deg_2_rad(),
find_curve_elbow(),
get_2d_angle(),
get_3d_angle(),
get_3d_cross_prod(),
get_dist_point_line(),
get_traj_velocities(),
get_velocity()
## One input rad_2_deg(pi/2) ## Multiple inputs rad_2_deg(c(pi / 2, pi, 2 * pi))## One input rad_2_deg(pi/2) ## Multiple inputs rad_2_deg(c(pi / 2, pi, 2 * pi))
read_flydra_mat() is designed to import data from a .mat file
that has been exported from Flydra software. The resultant object is a tibble
that additionally has important metadata stored as attributes (see Details).
read_flydra_mat(mat_file, file_id = NA, subject_name, frame_rate = 100, ...)read_flydra_mat(mat_file, file_id = NA, subject_name, frame_rate = 100, ...)
mat_file |
A file (or path to file) in .mat format, exported from Flydra |
file_id |
(Optional) identifier for this file. If not supplied, this
defaults to |
subject_name |
Name that will be assigned to the subject |
frame_rate |
The capture frame rate of the session |
... |
Additional arguments that may be passed from other pathviewr functions |
A tibble with numerical data in columns. The first two columns will
have frame numbers and time (assumed to be in secs), respectively. Columns
3 through 5 will contain position data. Note that unlike the behavior of
read_motive_csv() this function produces "tidy" data that have
already been gathered into key-value pairs based on subject.
Vikram B. Baliga
read_motive_csv for importing Motive data
Other data import functions:
as_viewr(),
import_and_clean_batch(),
import_batch(),
read_motive_csv()
library(pathviewr) ## Import the example Flydra data included in the package flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_wooster") ## Names of variables in the resulting tibble names(flydra_data) ## A variety of metadata are stored as attributes. Of particular interest: attr(flydra_data, "pathviewr_steps")library(pathviewr) ## Import the example Flydra data included in the package flydra_data <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_wooster") ## Names of variables in the resulting tibble names(flydra_data) ## A variety of metadata are stored as attributes. Of particular interest: attr(flydra_data, "pathviewr_steps")
read_motive_csv() is designed to import data from a CSV that has been
exported from Optitrack's Motive software. The resultant object is a tibble
that additionally has important metadata stored as attributes (see Details).
read_motive_csv(file_name, file_id = NA, simplify_marker_naming = TRUE, ...)read_motive_csv(file_name, file_id = NA, simplify_marker_naming = TRUE, ...)
file_name |
A file (or path to file) in CSV format |
file_id |
(Optional) identifier for this file. If not supplied, this
defaults to |
simplify_marker_naming |
If Markers are encountered, should they be renamed from "Subject:marker" to "marker"? Defaults to TRUE |
... |
Additional arguments passed from other |
Uses data.table::fread() to import data from a CSV file and
ultimately store it in a tibble. This object is also labeled with the
attribute pathviewr_steps with value viewr to indicate that it
has been imported by pathviewr and should be friendly towards use with
other functions in our package. Additionally, the following metadata are
stored in the tibble's attributes: header information from the Motive CSV
file (header), original IDs for each object (Motive_IDs), the
name of each subject in each data column (subject_names_full) and
unique values of subject names (subject_names_simple), the type of
data (rigid body or marker) that appears in each column
(data_types_full) and overall (data_types_simple), and original
data column names in the CSV (d1, d2). See Example below for example
code to inspect attributes.
A tibble with numerical data in columns. The first two columns will have frame numbers and time (assumed to be in secs), respectively. Columns 3 and beyond will contain the numerical data on the position or rotation of rigid bodies and/or markers that appear in the Motive CSV file. Each row corresponds to the position or rotation of all objects at a given time (frame).
This function was written to read CSVs exported using Motive's Format Version 1.23 and is not guaranteed to work with those from other versions. Please file an Issue on our Github page if you encounter any problems.
Vikram B. Baliga
read_flydra_mat for importing Flydra data
Other data import functions:
as_viewr(),
import_and_clean_batch(),
import_batch(),
read_flydra_mat()
library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Names of variables in the resulting tibble names(motive_data) ## A variety of metadata are stored as attributes. Of particular interest: attr(motive_data, "pathviewr_steps") attr(motive_data, "file_id") attr(motive_data, "header") attr(motive_data, "Motive_IDs") attr(motive_data, "subject_names_full") attr(motive_data, "subject_names_simple") attr(motive_data, "motive_data_names") attr(motive_data, "motive_data_types_full") attr(motive_data, "motive_data_types_simple") ## Of course, all attributes can be viewed as a (long) list via: attributes(motive_data)library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Names of variables in the resulting tibble names(motive_data) ## A variety of metadata are stored as attributes. Of particular interest: attr(motive_data, "pathviewr_steps") attr(motive_data, "file_id") attr(motive_data, "header") attr(motive_data, "Motive_IDs") attr(motive_data, "subject_names_full") attr(motive_data, "subject_names_simple") attr(motive_data, "motive_data_names") attr(motive_data, "motive_data_types_full") attr(motive_data, "motive_data_types_simple") ## Of course, all attributes can be viewed as a (long) list via: attributes(motive_data)
Redefine the center (0, 0, 0,) of the tunnel data via translating
positions along axes.
redefine_tunnel_center( obj_name, axes = c("position_length", "position_width", "position_height"), length_method = c("original", "middle", "median", "user-defined"), width_method = c("original", "middle", "median", "user-defined"), height_method = c("original", "middle", "median", "user-defined"), length_zero = NA, width_zero = NA, height_zero = NA, ... )redefine_tunnel_center( obj_name, axes = c("position_length", "position_width", "position_height"), length_method = c("original", "middle", "median", "user-defined"), width_method = c("original", "middle", "median", "user-defined"), height_method = c("original", "middle", "median", "user-defined"), length_zero = NA, width_zero = NA, height_zero = NA, ... )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
axes |
Names of axes to be centered |
length_method |
Method for length |
width_method |
Method for width |
height_method |
Method for height |
length_zero |
User-defined value |
width_zero |
User-defined value |
height_zero |
User-defined value |
... |
Additional arguments passed to/from other pathviewr functions |
For each _method argument, there are four choices of how centering is
handled: 1) "original" keeps axis as is – this is how width and (possibly)
height should be handled for flydra data; 2) "middle" is the middle of the
range of data: (min + max) / 2; 3) "median" is the median value of data on
that axis. Probably not recommended; and 4) "user-defined" lets the user
customize where the (0, 0, 0) point in the tunnel will end up. Each
_zero argument is subtracted from its corresponding axis' data.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") in which data have
been translated according to the user's inputs, generally with (0, 0,
0,) being relocated to the center of the tunnel.
Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
quick_separate_trajectories(),
relabel_viewr_axes(),
rename_viewr_characters(),
rotate_tunnel(),
select_x_percent(),
separate_trajectories(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
Other tunnel standardization functions:
rotate_tunnel(),
standardize_tunnel()
## Import the Flydra example data included in ## the package flydra_data <- read_flydra_mat( system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_wooster" ) ## Re-center the Flydra data set. ## Width will be untouched ## Length will use the "middle" definition ## And height will be user-defined to be ## zeroed at 1.44 on the original axis flydra_centered <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44)## Import the Flydra example data included in ## the package flydra_data <- read_flydra_mat( system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_wooster" ) ## Re-center the Flydra data set. ## Width will be untouched ## Length will use the "middle" definition ## And height will be user-defined to be ## zeroed at 1.44 on the original axis flydra_centered <- flydra_data %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44)
Axes are commonly labeled as "x", "y", and "z" in recording software yet
pathviewr functions require these to be labeled as "length", "width",
and "height". relabel_viewr_axes() is a function that takes a
viewr object and allows the user to rename its variables.
relabel_viewr_axes( obj_name, tunnel_length = "_z", tunnel_width = "_x", tunnel_height = "_y", real = "_w", ... )relabel_viewr_axes( obj_name, tunnel_length = "_z", tunnel_width = "_x", tunnel_height = "_y", real = "_w", ... )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
tunnel_length |
The dimension that corresponds to tunnel length. Set to
|
tunnel_width |
The dimension that corresponds to tunnel width. Follows
the same conventions as |
tunnel_height |
The dimension that corresponds to tunnel height. Follows
the same conventions as |
real |
The dimension that corresponds to the "real" parameter in
quaternion notation (for data with "rotation" values). Follows the same
conventions as |
... |
Additional arguments to be passed to |
Each argument must have a leading underscore ("_") and each argument must have an entry. E.g. tunnel_length = "_Y" will replace all instances of _Y with _length in the names of variables.
A tibble or data.frame with variables that have been renamed.
Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
quick_separate_trajectories(),
redefine_tunnel_center(),
rename_viewr_characters(),
rotate_tunnel(),
select_x_percent(),
separate_trajectories(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
library(pathviewr) ## Import the Motive example data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Names of variables are labeled with _x, _y, _z, which we'd like to rename names(motive_data) ## Now use relabel_viewr_axes() to rename these variables using _length, ## _width, and _height instead motive_data_relabeled <- relabel_viewr_axes(motive_data, tunnel_length = "_z", tunnel_width = "_x", tunnel_height = "_y", real = "_w") ## See the result names(motive_data_relabeled)library(pathviewr) ## Import the Motive example data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Names of variables are labeled with _x, _y, _z, which we'd like to rename names(motive_data) ## Now use relabel_viewr_axes() to rename these variables using _length, ## _width, and _height instead motive_data_relabeled <- relabel_viewr_axes(motive_data, tunnel_length = "_z", tunnel_width = "_x", tunnel_height = "_y", real = "_w") ## See the result names(motive_data_relabeled)
Remove any duplicates or aliased frames within trajectories
remove_duplicate_frames(obj_name)remove_duplicate_frames(obj_name)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
The separate_trajectories() and get_full_trajectories() must be run prior to use.
A viewr object (tibble or data.frame with attribute
pathviewr_steps.
Vikram B. Baliga
Other utility functions:
clean_by_span(),
insert_treatments(),
remove_vel_anomalies(),
set_traj_frametime()
Remove any rows which show sharp shifts in velocity that are likely due to tracking errors
remove_vel_anomalies( obj_name, target = "velocity", method = "gesd", alpha = 0.05, max_anoms = 0.2 )remove_vel_anomalies( obj_name, target = "velocity", method = "gesd", alpha = 0.05, max_anoms = 0.2 )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
target |
The column to target; defaults to "velocity" |
method |
The anomaly detection method; see anomalize::anomalize() |
alpha |
The width of the "normal" range; see anomalize::anomalize() |
max_anoms |
The max proportion of anomalies; see anomalize::anomalize() |
This function runs anomalize::anomalize() on a per-trajectory basis. The separate_trajectories() and get_full_trajectories() must be run prior to use.
A viewr object (tibble or data.frame with attribute
pathviewr_steps. Rows in which large anomalies were detected have
been removed. No additional columns are created.
Vikram B. Baliga
Other utility functions:
clean_by_span(),
insert_treatments(),
remove_duplicate_frames(),
set_traj_frametime()
Quick utility function to use str_replace with mutate(across()) to batch- rename subjects via pattern detection.
rename_viewr_characters( obj_name, target_column = "subject", pattern, replacement = "" )rename_viewr_characters( obj_name, target_column = "subject", pattern, replacement = "" )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
target_column |
The target column; defaults to "subject" |
pattern |
The (regex) pattern to be replaced |
replacement |
The replacement text. Must be a character |
A tibble or data frame in which subjects have been renamed according
to the pattern and replacement supplied by the user.
Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
quick_separate_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rotate_tunnel(),
select_x_percent(),
separate_trajectories(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "gather" step before running rescale_tunnel_data(). motive_gathered <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() ## See the subject names unique(motive_gathered$subject) ## Now rename the subjects. We'll get rid of "device" and replace it ## with "subject" motive_renamed <- motive_gathered %>% rename_viewr_characters(target_column = "subject", pattern = "device", replacement = "subject") ## See the new subject names unique(motive_renamed$subject)## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "gather" step before running rescale_tunnel_data(). motive_gathered <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() ## See the subject names unique(motive_gathered$subject) ## Now rename the subjects. We'll get rid of "device" and replace it ## with "subject" motive_renamed <- motive_gathered %>% rename_viewr_characters(target_column = "subject", pattern = "device", replacement = "subject") ## See the new subject names unique(motive_renamed$subject)
viewr objectShould data have been exported at an incorrect scale, apply an isometric transformation to the position data and associated mean marker errors (if found)
rescale_tunnel_data(obj_name, original_scale = 0.5, desired_scale = 1, ...)rescale_tunnel_data(obj_name, original_scale = 0.5, desired_scale = 1, ...)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
original_scale |
The original scale at which data were exported. See Details if unknown. |
desired_scale |
The desired scale |
... |
Additional arguments passed to/from other pathviewr functions |
The desired_scale is divided by the original_scale to
determine a scale_ratio internally. If the original_scale is
not explicitly known, set it to 1 and then set desired_scale to be
whatever scaling ratio you have in mind. E.g. setting original_scale
to 1 and then desired_scale to 0.7 will multiply all position axis
values by 0.7.
The default arguments of original_scale = 0.5 and
desired_scale = 1 apply a doubling of tunnel size isometrically.
A viewr object that has position data (and
mean_marker_error data, if found) adjusted by the ratio of
desired_scale/original_scale.
Vikram B. Baliga
## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "gather" step before running rescale_tunnel_data(). motive_gathered <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() ## Now rescale the tunnel data motive_rescaled <- motive_gathered %>% rescale_tunnel_data(original_scale = 0.5, desired_scale = 1) ## See the difference in data range e.g. for length range(motive_rescaled$position_length) range(motive_gathered$position_length)## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "gather" step before running rescale_tunnel_data(). motive_gathered <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() ## Now rescale the tunnel data motive_rescaled <- motive_gathered %>% rescale_tunnel_data(original_scale = 0.5, desired_scale = 1) ## See the difference in data range e.g. for length range(motive_rescaled$position_length) range(motive_gathered$position_length)
Specify a minimum number of trajectories that each subject must complete during a treatment, trial, or session.
rm_by_trajnum( obj_name, trajnum = 5, mirrored = FALSE, treatment1, treatment2, ... )rm_by_trajnum( obj_name, trajnum = 5, mirrored = FALSE, treatment1, treatment2, ... )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
trajnum |
Minimum number of trajectories; must be numeric. |
mirrored |
Does the data have mirrored treatments? If so, arguments
|
treatment1 |
The first treatment or session during which the threshold must be met. |
treatment2 |
A second treatment or session during which the threshold must be met. |
... |
Additional arguments passed to/from other pathviewr functions. |
Depending on analysis needs, users may want to remove subjects that
have not completed a certain number of trajectories during a treatment,
trial, or session. If mirrored = FALSE, no treatment information is
necessary and subjects will be removed based on total number of trajectories
as specified in trajnum. If mirrored = TRUE, the
treatment1 and treatment2 parameters will allow users to
define during which treatments or sessions subjects must reach threshold as
specified in the trajnum argument. For example, if mirrored =
TRUE, setting treatment1 = "latA", treatment2 = "latB" and
trajnum = 5 will remove subjects that have fewer than 5 trajectories
during the "latA" treatment AND the "latB" treatment.
treatment1 and treatment2 should be levels within a column
named "treatment".
A viewr object; a tibble or data.frame with attribute
pathviewr_steps that includes "viewr" that now has fewer
observations (rows) as a result of removal of subjects with too few
trajectories according to the trajnum parameter.
Melissa S. Armstrong
library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean, isolate, and label trajectories motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ##Remove subjects that have not completed at least 150 trajectories: motive_rm_unmirrored <- motive_full %>% rm_by_trajnum(trajnum = 150) ## Add treatment information motive_full$treatment <- c(rep("latA", 100), rep("latB", 100), rep("latA", 100), rep("latB", 149)) ## Remove subjects by that have not completed at least 10 trajectories in ## both treatments motive_rm_mirrored <- motive_full %>% rm_by_trajnum( trajnum = 10, mirrored = TRUE, treatment1 = "latA", treatment2 = "latB" )library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean, isolate, and label trajectories motive_full <- motive_data %>% clean_viewr(desired_percent = 50, max_frame_gap = "autodetect", span = 0.95) ##Remove subjects that have not completed at least 150 trajectories: motive_rm_unmirrored <- motive_full %>% rm_by_trajnum(trajnum = 150) ## Add treatment information motive_full$treatment <- c(rep("latA", 100), rep("latB", 100), rep("latA", 100), rep("latB", 149)) ## Remove subjects by that have not completed at least 10 trajectories in ## both treatments motive_rm_mirrored <- motive_full %>% rm_by_trajnum( trajnum = 10, mirrored = TRUE, treatment1 = "latA", treatment2 = "latB" )
The rotation is applied about the height axis and affects tunnel length and width only, i.e. no rotation of height.
rotate_tunnel( obj_name, all_heights_min = 0.11, all_heights_max = 0.3, perch1_len_min = -0.06, perch1_len_max = 0.06, perch2_len_min = 2.48, perch2_len_max = 2.6, perch1_wid_min = 0.09, perch1_wid_max = 0.31, perch2_wid_min = 0.13, perch2_wid_max = 0.35, ... )rotate_tunnel( obj_name, all_heights_min = 0.11, all_heights_max = 0.3, perch1_len_min = -0.06, perch1_len_max = 0.06, perch2_len_min = 2.48, perch2_len_max = 2.6, perch1_wid_min = 0.09, perch1_wid_max = 0.31, perch2_wid_min = 0.13, perch2_wid_max = 0.35, ... )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
all_heights_min |
Minimum perch height |
all_heights_max |
Maximum perch height |
perch1_len_min |
Minimum length value of perch 1 |
perch1_len_max |
Maximum length value of perch 1 |
perch2_len_min |
Minimum length value of perch 2 |
perch2_len_max |
Maximum length value of perch 2 |
perch1_wid_min |
Minimum width value of perch 1 |
perch1_wid_max |
Maximum width value of perch 1 |
perch2_wid_min |
Minimum width value of perch 2 |
perch2_wid_max |
Maximum width value of perch 2 |
... |
Additional arguments passed to/from other pathviewr functions |
The user first estimates the locations of the perches by specifying bounds for where each perch is located. The function then computes the center of each bounding box and estimates that to be the midpoint of each perch. Then the center point of the tunnel (center between the perch midpoints) is estimated. The angle between perch1_center, tunnel_center_point, and arbitrary point along the length axis (tunnel_center_point - 1 on length) is estimated. That angle is then used to rotate the data, again only in the length and width dimensions. Height is standardized by (approximate) perch height; values greater than 0 are above the perch and values less than 0 are below the perch level.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") in which data have
been rotated according to user specifications.
Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
quick_separate_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rename_viewr_characters(),
select_x_percent(),
separate_trajectories(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
Other tunnel standardization functions:
redefine_tunnel_center(),
standardize_tunnel()
## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "trimmed" step before running rotate_tunnel(). motive_trimmed <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() ## Now rotate the tunnel using default values motive_rotated <- motive_trimmed %>% rotate_tunnel() ## The following attributes store information about ## how rotation & translation was applied attr(motive_rotated, "rotation_degrees") attr(motive_rotated, "rotation_radians") attr(motive_rotated, "perch1_midpoint_original") attr(motive_rotated, "perch1_midpoint_current") attr(motive_rotated, "tunnel_centerpoint_original") attr(motive_rotated, "perch2_midpoint_original") attr(motive_rotated, "perch2_midpoint_current")## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "trimmed" step before running rotate_tunnel(). motive_trimmed <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() ## Now rotate the tunnel using default values motive_rotated <- motive_trimmed %>% rotate_tunnel() ## The following attributes store information about ## how rotation & translation was applied attr(motive_rotated, "rotation_degrees") attr(motive_rotated, "rotation_radians") attr(motive_rotated, "perch1_midpoint_original") attr(motive_rotated, "perch1_midpoint_current") attr(motive_rotated, "tunnel_centerpoint_original") attr(motive_rotated, "perch2_midpoint_original") attr(motive_rotated, "perch2_midpoint_current")
Chop data into X sections (of equal size) along a specified axis
section_tunnel_by(obj_name, axis = "position_length", number_of_sections = 20)section_tunnel_by(obj_name, axis = "position_length", number_of_sections = 20)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
axis |
Chosen axis, must match name of column exactly |
number_of_sections |
Total number of sections |
The idea is to bin the data along a specified axis, generally
position_length.
A new column added to the input data object called section_id,
which is an ordered factor that indicates grouping.
Vikram B. Baliga
## Load data and run section_tunnel_by() test_mat <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_wooster") %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = 1) %>% get_full_trajectories(span = 0.95) %>% section_tunnel_by(number_of_sections = 10) ## Plot; color by section ID plot(test_mat$position_length, test_mat$position_width, asp = 1, col = as.factor(test_mat$section_id))## Load data and run section_tunnel_by() test_mat <- read_flydra_mat(system.file("extdata", "pathviewr_flydra_example_data.mat", package = 'pathviewr'), subject_name = "birdie_wooster") %>% redefine_tunnel_center(length_method = "middle", height_method = "user-defined", height_zero = 1.44) %>% select_x_percent(desired_percent = 50) %>% separate_trajectories(max_frame_gap = 1) %>% get_full_trajectories(span = 0.95) %>% section_tunnel_by(number_of_sections = 10) ## Plot; color by section ID plot(test_mat$position_length, test_mat$position_width, asp = 1, col = as.factor(test_mat$section_id))
Select data in the middle X percent of the length of the tunnel
select_x_percent(obj_name, desired_percent = 33, ...)select_x_percent(obj_name, desired_percent = 33, ...)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
desired_percent |
Numeric, the percent of the total length of the tunnel that will define the region of interest. Measured from the center outwards. |
... |
Additional arguments passed to/from other pathviewr functions |
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") in which data outside
the region of interest have been removed.
Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
quick_separate_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rename_viewr_characters(),
rotate_tunnel(),
separate_trajectories(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "trimmed" step before running rotate_tunnel(). motive_rotated <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() ## Now select the middle 50% of the tunnel motive_selected <- motive_rotated %>% select_x_percent(desired_percent = 50) ## Compare the ranges of lengths to see the effect range(motive_rotated$position_length) range(motive_selected$position_length)motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "trimmed" step before running rotate_tunnel(). motive_rotated <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() ## Now select the middle 50% of the tunnel motive_selected <- motive_rotated %>% select_x_percent(desired_percent = 50) ## Compare the ranges of lengths to see the effect range(motive_rotated$position_length) range(motive_selected$position_length)
Separate rows of data into separately labeled trajectories.
separate_trajectories( obj_name, max_frame_gap = 1, frame_rate_proportion = 0.1, frame_gap_messaging = FALSE, frame_gap_plotting = FALSE, ... )separate_trajectories( obj_name, max_frame_gap = 1, frame_rate_proportion = 0.1, frame_gap_messaging = FALSE, frame_gap_plotting = FALSE, ... )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
max_frame_gap |
Default 1; defines the largest permissible gap in data before a new trajectory is forced to be defined. Can be either a numeric value or "autodetect". See Details for more. |
frame_rate_proportion |
Default 0.10; if |
frame_gap_messaging |
Default FALSE; should frame gaps be reported in the console? |
frame_gap_plotting |
Default FALSE; should frame gap diagnostic plots be shown? |
... |
Additional arguments passed to/from other pathviewr functions |
This function is designed to separate rows of data into separately labeled trajectories.
The max_frame_gap parameter determines how trajectories will be
separated. If numeric, the function uses the supplied value as the largest
permissible gap in frames before a new trajectory is defined.
If max_frame_gap = "autodetect" is used, the function
attempts to guesstimate the best value(s) of max_frame_gap. This is
performed separately for each subject in the data set, i.e. as many
max_frame_gap values will be estimated as there are unique subjects.
The highest possible value of any max_frame_gap is first set as a
proportion of the capture frame rate, as defined by the
frame_rate_proportion parameter (default 0.10). For each subject, a
plot of total trajectory counts vs. max frame gap values is created
internally (but can be plotted via setting
frame_gap_plotting = TRUE). As larger max frame gaps are allowed,
trajectory count will necessarily decrease but may reach a value that
likely represents the "best" option. The "elbow" of that plot is then used
to find an estimate of the best max frame gap value to use.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") in which a new column
file_sub_traj is added, which labels trajectories within the data by
concatenating file name, subject name, and a trajectory number (all
separated by underscores).
Vikram B. Baliga and Melissa S. Armstrong
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
quick_separate_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rename_viewr_characters(),
rotate_tunnel(),
select_x_percent(),
standardize_tunnel(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
Other functions that define or clean trajectories:
get_full_trajectories(),
quick_separate_trajectories(),
visualize_frame_gap_choice()
## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "select" step before running select_x_percent(). motive_selected <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) ## Now separate trajectories using autodetect motive_separated <- motive_selected %>% separate_trajectories(max_frame_gap = "autodetect", frame_rate_proportion = 0.1) ## See new column file_sub_traj for trajectory labeling names(motive_separated)## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "select" step before running select_x_percent(). motive_selected <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% select_x_percent(desired_percent = 50) ## Now separate trajectories using autodetect motive_separated <- motive_selected %>% separate_trajectories(max_frame_gap = "autodetect", frame_rate_proportion = 0.1) ## See new column file_sub_traj for trajectory labeling names(motive_separated)
After a data set has been separated into trajectories, find the earliest frame in each trajectory and set the corresponding time to 0. All subsequent time_sec stamps are computed according to successive frame numbering.
set_traj_frametime(obj_name)set_traj_frametime(obj_name)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
The separate_trajectories() and get_full_trajectories() must be run prior to use. The initial traj_time and traj_frame values are set to 0 within each trajectory.
A viewr object (tibble or data.frame with attribute
pathviewr_steps. New columns include traj_time (the
trajectory-specific time values) and traj_frame (the trajectory-specific
frame numbering).
Vikram B. Baliga
Other utility functions:
clean_by_span(),
insert_treatments(),
remove_duplicate_frames(),
remove_vel_anomalies()
Similar to rotate_tunnel(); rotate and center tunnel data based on
landmarks (specific subjects in the data).
standardize_tunnel( obj_name, landmark_one = "perch1", landmark_two = "perch2", ... )standardize_tunnel( obj_name, landmark_one = "perch1", landmark_two = "perch2", ... )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
landmark_one |
Subject name of the first landmark |
landmark_two |
Subject name of the second landmark |
... |
Additional arguments passed to/from other pathviewr functions |
The center point of the tunnel is estimated as the point between the
two landmarks. It is therefore recommended that landmark_one and
landmark_two be objects that are placed on opposite ends of the
tunnel (e.g. in an avian flight tunnel, these landmarks may be perches that
are placed at the extreme ends). The angle between landmark_one,
tunnel_center_point, and arbitrary point along the length axis
(tunnel_center_point - 1 on length) is estimated. That angle is then used
to rotate the data, again only in the length and width dimensions. Height
is standardized by average landmark height; values greater than 0 are above
the landmarks and values less than 0 are below the landmark level.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") in which data have
been rotated according to the positions of the landmarks in the data.
The position_length values of landmark_one MUST be less than
the position_length values of landmark_two; otherwise,
the rotation will apply to a mirror-image of the tunnel
Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
quick_separate_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rename_viewr_characters(),
rotate_tunnel(),
select_x_percent(),
separate_trajectories(),
trim_tunnel_outliers(),
visualize_frame_gap_choice()
Other tunnel standardization functions:
redefine_tunnel_center(),
rotate_tunnel()
## Example data that would work with this function are ## not included in this version of pathviewr. Please ## contact the package authors for further guidance ## should you need it.## Example data that would work with this function are ## not included in this version of pathviewr. Please ## contact the package authors for further guidance ## should you need it.
The user provides estimates of min and max values of data. This function then trims out anything beyond these estimates.
trim_tunnel_outliers( obj_name, lengths_min = 0, lengths_max = 3, widths_min = -0.4, widths_max = 0.8, heights_min = -0.2, heights_max = 0.5, ... )trim_tunnel_outliers( obj_name, lengths_min = 0, lengths_max = 3, widths_min = -0.4, widths_max = 0.8, heights_min = -0.2, heights_max = 0.5, ... )
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
lengths_min |
Minimum length |
lengths_max |
Maximum length |
widths_min |
Minimum width |
widths_max |
Maximum width |
heights_min |
Minimum height |
heights_max |
Maximum height |
... |
Additional arguments passed to/from other pathviewr functions |
Anything supplied to _min or _max arguments should be single numeric values.
A viewr object (tibble or data.frame with attribute
pathviewr_steps that includes "viewr") in which data outside
the specified ranges has been excluded.
Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
quick_separate_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rename_viewr_characters(),
rotate_tunnel(),
select_x_percent(),
separate_trajectories(),
standardize_tunnel(),
visualize_frame_gap_choice()
## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "gather" step before running trim_tunnel_outliers(). motive_gathered <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() ## Now trim outliers using default values motive_trimmed <- motive_gathered %>% trim_tunnel_outliers(lengths_min = 0, lengths_max = 3, widths_min = -0.4, widths_max = 0.8, heights_min = -0.2, heights_max = 0.5)## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) ## Clean the file. It is generally recommended to clean up to the ## "gather" step before running trim_tunnel_outliers(). motive_gathered <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() ## Now trim outliers using default values motive_trimmed <- motive_gathered %>% trim_tunnel_outliers(lengths_min = 0, lengths_max = 3, widths_min = -0.4, widths_max = 0.8, heights_min = -0.2, heights_max = 0.5)
Run separate_trajectories() with many different frame gaps to help determine what value to use
visualize_frame_gap_choice(obj_name, loops = 20, ...)visualize_frame_gap_choice(obj_name, loops = 20, ...)
obj_name |
The input viewr object; a tibble or data.frame with attribute
|
loops |
How many total frame gap entries to consider. Each loop will
increase the |
... |
Additional arguments |
The input viewr object (obj_name) should likely be an object
that has passed through the select_x_percent() step.
A plot and a tibble, each of which shows the total number of
trajectories that result from using the specified range of
max_frame_gap values.
Melissa S. Armstrong and Vikram B. Baliga
Other data cleaning functions:
gather_tunnel_data(),
get_full_trajectories(),
quick_separate_trajectories(),
redefine_tunnel_center(),
relabel_viewr_axes(),
rename_viewr_characters(),
rotate_tunnel(),
select_x_percent(),
separate_trajectories(),
standardize_tunnel(),
trim_tunnel_outliers()
Other plotting functions:
plot_by_subject(),
plot_viewr_trajectories()
Other functions that define or clean trajectories:
get_full_trajectories(),
quick_separate_trajectories(),
separate_trajectories()
library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) motive_selected <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% get_velocity() %>% select_x_percent(desired_percent = 50) visualize_frame_gap_choice(motive_selected, loops = 10)library(pathviewr) ## Import the example Motive data included in the package motive_data <- read_motive_csv(system.file("extdata", "pathviewr_motive_example_data.csv", package = 'pathviewr')) motive_selected <- motive_data %>% relabel_viewr_axes() %>% gather_tunnel_data() %>% trim_tunnel_outliers() %>% rotate_tunnel() %>% get_velocity() %>% select_x_percent(desired_percent = 50) visualize_frame_gap_choice(motive_selected, loops = 10)