Type:	Package
Title:	Analysis and Visualisation of Musical Audio and Video Movement Synchrony Data
Version:	0.1.4
Description:	Analysis and visualisation of synchrony, interaction, and joint movements from audio and video movement data of a group of music performers. The demo is data described in Clayton, Leante, and Tarsitani (2021) <doi:10.17605/OSF.IO/KS325>, while example analyses can be found in Clayton, Jakubowski, and Eerola (2019) <doi:10.1177/1029864919844809>. Additionally, wavelet analysis techniques have been applied to examine movement-related musical interactions, as shown in Eerola et al. (2018) <doi:10.1098/rsos.171520>.
License:	MIT + file LICENSE
Depends:	R (≥ 2.10)
Imports:	circular, dplyr, ggplot2, gridExtra, hms, igraph, lmtest, methods, osfr, rlang, scales, signal, tidyr, WaveletComp, zoo
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0)
Config/testthat/edition:	3
Encoding:	UTF-8
LazyData:	false
RoxygenNote:	7.2.3
NeedsCompilation:	no
Packaged:	2023-06-09 09:52:04 UTC; tuomaseerola
Author:	Tuomas Eerola [aut, cre, cph], Martin Clayton [aut], Paul Emms [aut]
Maintainer:	Tuomas Eerola <tuomas.eerola@durham.ac.uk>
Repository:	CRAN
Date/Publication:	2023-06-09 14:10:05 UTC

NIR_ABh_Puriya_Annotation

Description

A subset of data from NIR_ABh_Puriya annotation. The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Usage

data(NIR_ABh_Puriya_Annotation)

Format

rda

A data frame with 161 rows and 5 columns:

START-END

Type of annotation

2nd colum

Onset of annotation

3rd colum

Offset of annotation

4th colum

Duration of annotation

5th colum

Description

...

Source

https://osf.io/tj2n5

NIR_ABh_Puriya_Annotation_Influence

Description

A subset of data from NIR_ABh_Puriya describing the annotated influence. The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Usage

data(NIR_ABh_Puriya_Annotation_Influence)

Format

rda

A data frame with 306 rows and 5 columns:

Event

Type of event

Onset time

Start of the event in seconds

Offset time

End of the event in seconds

Duration

Duration of the event in seconds

Notes

Text notes

...

Source

https://osf.io/ks325/

NIR_ABh_Puriya_Central_Feature_Sitar

Description

A subset of data from NIR_ABh_Puriya describing sitar pitch. Dummy data for demonstration purposes. The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Usage

data(NIR_ABh_Puriya_Central_Feature_Sitar)

Format

rda

A data frame with 1,501 rows and 3 columns:

X

Frame (here 25 fps)

Pitch

Pitch in Hz - Dummy data

Smooth

Smooth - Dummy data

...

Source

https://osf.io/tj2n5

NIR_ABh_Puriya_Central_Pose_Sitar

Description

A subset of data from NIR_ABh_Puriya the estimate pose of the sitar player, carried out with openpose. The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Usage

data(NIR_ABh_Puriya_Central_Pose_Sitar)

Format

rda

A data frame with 1,501 rows and 27 columns:

X

frame number, 25 fps

LEar_x

X coordinate of Left Ear

LEar_y

Y coordinate of Left Ear

LElbow_x

X coordinate of Left Elbow

LElbow_y

Y coordinate of Left Elbow

LEye_x

X coordinate of Left Eye

LEye_y

Y coordinate of Left Eye

LShoulder_x

X coordinate of Left Shoulder

LShoulder_y

Y coordinate of Left Shoulder

LWrist_x

X coordinate of Left Wrist

LWrist_y

Y coordinate of Left Wrist

MidHip_x

X coordinate of Left MidHip

MidHip_y

Y coordinate of Left MidHip

Neck_x

X coordinate of Left Neck

Neck_y

Y coordinate of Left Neck

Nose_x

X coordinate of Left Nose

Nose_y

Y coordinate of Left Nose

REar_x

X coordinate of Right Ear

REar_y

Y coordinate of Right Ear

RElbow_x

X coordinate of Right Elbow

RElbow_y

Y coordinate of Right Elbow

REye_x

X coordinate of Right Eye

REye_y

Y coordinate of Right Eye

RShoulder_x

X coordinate of Right Shoulder

RShoulder_y

Y coordinate of Right Shoulder

RWrist_x

X coordinate of Right Wrist

RWrist_y

Y coordinate of Right Wrist

...

Source

https://osf.io/tj2n5

NIR_ABh_Puriya_Central_Pose_Tabla

Description

A subset of data from NIR_ABh_Puriya the estimate pose of the tabla player. The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Usage

data(NIR_ABh_Puriya_Central_Pose_Tabla)

Format

rda

A data frame with 1,501 rows and 27 columns:

X

frame number, here 25 fps

LEar_x

X coordinate of Left Ear

LEar_y

Y coordinate of Left Ear

LElbow_x

X coordinate of Left Elbow

LElbow_y

Y coordinate of Left Elbow

LEye_x

X coordinate of Left Eye

LEye_y

Y coordinate of Left Eye

LShoulder_x

X coordinate of Left Shoulder

LShoulder_y

Y coordinate of Left Shoulder

LWrist_x

X coordinate of Left Wrist

LWrist_y

Y coordinate of Left Wrist

MidHip_x

X coordinate of Left MidHip

MidHip_y

Y coordinate of Left MidHip

Neck_x

X coordinate of Left Neck

Neck_y

Y coordinate of Left Neck

Nose_x

X coordinate of Left Nose

Nose_y

Y coordinate of Left Nose

REar_x

X coordinate of Right Ear

REar_y

Y coordinate of Right Ear

RElbow_x

X coordinate of Right Elbow

RElbow_y

Y coordinate of Right Elbow

REye_x

X coordinate of Right Eye

REye_y

Y coordinate of Right Eye

RShoulder_x

X coordinate of Right Shoulder

RShoulder_y

Y coordinate of Right Shoulder

RWrist_x

X coordinate of Right Wrist

RWrist_y

Y coordinate of Right Wrist

...

Source

https://osf.io/tj2n5

NIR_ABh_Puriya_Metre_DrutTeental

Description

A subset of data from NIR_ABh_Puriya Describing Metre (Cycle numbers and onset times). The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Usage

data(NIR_ABh_Puriya_Metre_DrutTeental)

Format

rda

A data frame with 351 rows and 3 columns:

Cycle

Number of the Cycle

Time

Time in seconds

Notes

text which is empty for this file

...

Source

https://osf.io/fzv3k

NIR_ABh_Puriya_Metre_VilambitTeental

Description

A subset of data from NIR_ABh_Puriya describing the metre in Vilambit Teental section.

Usage

data(NIR_ABh_Puriya_Metre_VilambitTeental)

Format

rda

A data frame with 72 rows and 4 columns:

Cycle

Number of the Cycle

Time

Time in seconds

Notes

text which is empty for this file

Beats

Number of beats in the cycle

...

Details

The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Source

https://osf.io/dyu68

NIR_ABh_Puriya_Onsets_Selected_DrutTeental

Description

A subset of data from NIR_ABh_Puriya containing information about selected onsets for Drut Teental section. The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Usage

data(NIR_ABh_Puriya_Onsets_Selected_DrutTeental)

Format

rda

A data frame with 5,585 rows and 20 columns:

Session

Session name

Inst.Name

Instrument Name

Tala

Tala name

Label

Label for beat (1|1)

Matra

Matra number

Half.beat

logical On or Off

Half

integer (1) for logical on or Off

Misc.1

Descriptor e.g. 'Gat'

Misc.2

Another descriptor, usually missing

Cadence

Descriptor

Tabla.solo

Descriptor where N is 'No'

Inst

Onset time in seconds

Tabla

Onset time in seconds of tabla

Inst.Density

Calculated density of onsets (no/s)

Tabla.Density

Calculated density of onsets (no/s)

Inst.Peak

Peak of the onset (onset strength)

Tabla.Peak

Peak of the onset (onset strength)

Inst.Player

Name of the performer (sitar)

Tabla.Player

Name of the performer (tabla)

Chunk

Chunk name

...

Source

https://osf.io/phv6b

NIR_ABh_Puriya_Onsets_Selected_VilambitTeental

Description

A subset of data from NIR_ABh_Puriya containing information about selected onsets for Vilambit Teental section (sitar and tabla). The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Usage

data(NIR_ABh_Puriya_Onsets_Selected_VilambitTeental)

Format

rda

A data frame with 2,275 rows and 20 columns:

Session

Session name

Inst.Name

Instrument Name

Tala

Tala name

Label

Label for beat (1|1)

Matra

Matra number

Half.beat

logical On or Off

Half

integer (1) for logical on or Off

Misc.1

Descriptor e.g. 'Gat'

Misc.2

Another descriptor, usually missing

Cadence

Descriptor

Tabla.solo

Descriptor where N is 'No'

Inst

Onset time in seconds

Tabla

Onset time in seconds of tabla

Inst.Density

Calculated density of onsets (no/s)

Tabla.Density

Calculated density of onsets (no/s)

Inst.Peak

Peak of the onset (onset strength)

Tabla.Peak

Peak of the onset (onset strength)

Inst.Player

Name of the performer (sitar)

Tabla.Player

Name of the performer (tabla)

Chunk

Chunk name

...

Source

https://osf.io/xcefp

NIR_ABh_Puriya_OptFlow_Central_Sitar

Description

A subset of data from NIR_ABh_Puriya describing the head movement of the sitar player extracted using Optical Flow giving X and Y coordinates. The data comes from a collection of audiovisual recordings of North Indian (Hindustani) raga performances which are part of IEMP North Indian Raga collection, collected and curated by Martin Clayton, Laura Leante, and Simone Tarsitani.

Usage

data(NIR_ABh_Puriya_OptFlow_Central_Sitar)

Format

rda

A data frame with 1,501 rows and 4 columns:

Frame

Frame (integer, related 25 fps)

Time

Time in seconds

X

X coordinate

Y

Y coordinate

...

Source

https://osf.io/r4xza

Analyze Coherency from View object

Description

Analyze Coherency from View object

Usage

analyze_coherency(
  obj,
  columns,
  loess.span = 0,
  dj = 1/50,
  lowerPeriod = 2/obj$recording$fps,
  upperPeriod = 5,
  window.type.t = 1,
  window.type.s = 1,
  window.size.t = 5,
  window.size.s = 1/4,
  make.pval = TRUE,
  method = "white.noise",
  params = NULL,
  n.sim = 1,
  date.format = NULL,
  date.tz = NULL,
  verbose = FALSE
)

Arguments

Value

an analyze_coherency object.

See Also

Other wavelet functions: analyze_wavelet(), get_local_max_average_power(), plot_average_coherency(), plot_average_power(), plot_cross_spectrum(), plot_cwt_energy(), plot_phase_difference(), plot_power_spectrum(), plot_roll_resultant_length(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
co <- analyze_coherency(pv, c("Nose_x", "Nose_y"))

Analyze Wavelet from View object

Description

Analyze Wavelet from View object

Usage

analyze_wavelet(
  obj,
  column,
  loess.span = 0,
  dj = 1/20,
  lowerPeriod = 2/obj$recording$fps,
  upperPeriod = 5,
  make.pval = TRUE,
  method = "white.noise",
  params = NULL,
  n.sim = 1,
  date.format = NULL,
  date.tz = NULL,
  verbose = TRUE
)

Arguments

Value

an analyze.wavelet object.

See Also

Other wavelet functions: analyze_coherency(), get_local_max_average_power(), plot_average_coherency(), plot_average_power(), plot_cross_spectrum(), plot_cwt_energy(), plot_phase_difference(), plot_power_spectrum(), plot_roll_resultant_length(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
w <- analyze_wavelet(pv, "Nose_y")

Apply summary function to the columns in each segment of a SpliceView object

Description

Apply summary function to each data point column in a SplicedView and return list of output data.

Usage

apply_column_spliceview(sv, FUN, simplify = FALSE, USE.NAMES = FALSE, ...)

sapply_column_spliceview(sv, FUN, simplify = TRUE, USE.NAMES = TRUE, ...)

Arguments

Value

see sapply().

See Also

Other statistical and analysis functions: apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
d1 <- get_duration_annotation_data(r)
# only one relevant section for sample data
splicing_smile_df <- splice_time(d1, tier ='INTERACTION',
  comments = 'Mutual look and smile')

fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)
sv_duration_smile <- get_spliced_view(jv, splicing_df = splicing_smile_df)
mean_mat <- apply_column_spliceview(sv_duration_smile, mean, na.rm=TRUE)

Apply a filter to a View

Description

Apply a filter to a View

Usage

apply_filter(
  view,
  data_points,
  sig_filter,
  param_str = "",
  folder_out = "Filtered",
  save_output = FALSE
)

Arguments

Value

a filtered object.

Apply a Savitzky-Golay filter to a view

Description

Apply a Savitzky-Golay filter to a view

Usage

apply_filter_sgolay(
  view,
  data_points,
  n,
  p,
  folder_out = "Filtered",
  save_output = FALSE
)

Arguments

Value

a FilteredView object.

See Also

Other data functions: get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)

set.seed(1)
fv1 <- apply_filter_sgolay(pv, c("Nose", "RWrist", "LWrist"), n = 19, p = 4)
fv2 <- apply_filter_sgolay(pv, c("Nose", "RWrist", "LWrist"), n = 41, p = 3)

set.seed(1) # to reproduce with S3 filter object
fv3 <- apply_filter(pv, c("Nose", "RWrist", "LWrist"), signal::sgolay(4, 19))

Apply complex function to each segment in a SpliceView object

Description

Apply complex function to each segment in a SpliceView object

Usage

apply_segment_spliceview(sv, FUN, ...)

Arguments

Value

list of two elements: 'output' containing results of apply FUN to 'input'

See Also

Other statistical and analysis functions: apply_column_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
d1 <- get_duration_annotation_data(r)
# only one relevant section for sample data
splicing_smile_df <- splice_time(d1, tier ='INTERACTION',
  comments = 'Mutual look and smile')

fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)
sv_duration_smile <- get_spliced_view(jv, splicing_df = splicing_smile_df)
wavelet_smile_list <- apply_segment_spliceview(sv_duration_smile, analyze_wavelet,
  column = "Nose_x_Central_Sitar")
names(wavelet_smile_list)

Autolayer methods

Description

Layers of annotation data to add to ggplots in 'movementsync.

Usage

## S3 method for class 'OnsetsSelected'
autolayer(
  object,
  time_limits = c(-Inf, Inf),
  colour = "Inst.Name",
  fill = "Metre",
  alpha = 0.4,
  instrument_cols = NULL,
  ...
)

## S3 method for class 'Metre'
autolayer(
  object,
  time_limits = c(-Inf, Inf),
  colour = "hotpink",
  alpha = 0.5,
  tempo = FALSE,
  view = NULL,
  columns = NULL,
  ...
)

## S3 method for class 'Duration'
autolayer(
  object,
  time_limits = c(-Inf, Inf),
  expr = .data$Tier == "FORM",
  fill_column = "Comments",
  geom = "rect",
  vline_column = "In",
  ...
)

## S3 method for class 'Splice'
autolayer(object, geom = "rect", vline_column = "Start", ...)

Arguments

Value

ggplot geom object

Examples

r<-get_recording("NIR_ABh_Puriya", fps=25)
o <- get_onsets_selected_data(r)
v <- get_raw_view(r, "Central", "", "Sitar")
autoplot(v, columns = c("LEar_x", "LEar_y"), maxpts=5000) + autolayer(o)

m <- get_metre_data(r)
autoplot(v, columns = c("LEar_x", "LEar_y"), time_limits = c(1000, 2000)) +
  autolayer(m, time_limits = c(1000, 2000))
autoplot(v, columns = c("LEar_x", "LEar_y"), time_limits = c(1000, 2000)) +
  autolayer(m, tempo = TRUE, time_limits = c(1000, 2000), view = v,
            columns = c("LEar_x", "LEar_y"))

d <- get_duration_annotation_data(r)
autoplot(m)
autoplot(m) + autolayer(d)
autoplot(m) + autolayer(d, fill_col = "Tier")

v <- get_raw_view(r, "Central", "", "Sitar")
autoplot(v, columns = c("LEar_x", "LEar_y")) +
  autolayer(d)
autoplot(v, columns = c("LEar_x", "LEar_y")) +
  autolayer(d, expr = Tier == "FORM" & substr(Comments, 1, 1) == "J")
autoplot(v, columns = c("LEar_x", "LEar_y")) +
  autolayer(d, geom = "vline", nudge_x = -60, size = 3, colour = "blue")

Diagnostic plots

Description

Autoplot methods for S3 objects in the movementsync package.

Usage

## S3 method for class 'Duration'
autoplot(object, horizontal = FALSE, ...)

## S3 method for class 'OnsetsSelected'
autoplot(object, instrument = "Inst", tactus = "Matra", ...)

## S3 method for class 'Metre'
autoplot(object, ...)

## S3 method for class 'View'
autoplot(
  object,
  columns = NULL,
  maxpts = 1000,
  time_limits = c(-Inf, Inf),
  time_breaks = NULL,
  expr = NULL,
  ...
)

## S3 method for class 'SplicedView'
autoplot(
  object,
  columns = NULL,
  segments = NULL,
  time_breaks = NULL,
  time_limits = c(-Inf, Inf),
  maxpts = 1000,
  ...
)

Arguments

Value

a ggplot object.

Examples

r <- get_sample_recording()
d <- get_duration_annotation_data(r)
autoplot(d)
o <- get_onsets_selected_data(r)
autoplot(o)
m <- get_metre_data(r)
autoplot(m)
v <- get_raw_view(r, "Central", "", "Sitar")
autoplot(v, columns = c("LEar_x", "LEar_y"), time_limits = c(20, 40))
l <- list(a = c(0, 10), b = c(20, 30), c = c(30, 60))
splicing_df <- splice_time(l)
sv <- get_spliced_view(v, splicing_df)
autoplot(sv, columns = c("LEar_x", "LEar_y", "Nose_x", "Nose_y"), time_breaks = 4, maxpts = 1000)

Plot a Granger S3 object

Description

Plot a Granger S3 object

Usage

## S3 method for class 'GrangerTime'
autoplot(object, splicing_df, lev_sig = 0.05, ...)

Arguments

Value

a ggplot object.

See Also

Other Granger Causality: get_granger_interactions(), granger_test(), map_to_granger_test(), ms_condgrangertest(), ms_grangertest1(), ms_grangertest2(), plot.GrangerInteraction(), plot_influence_diagram()

Examples

r1 <- get_sample_recording()
fv_list <- get_filtered_views(r1, data_points = "Nose", n = 41, p = 3)
jv_sub <- get_joined_view(fv_list)
splicing_df <- splice_time(jv_sub, win_size = 3, step_size = 0.5)
sv <- get_spliced_view(jv_sub, splicing_df)
g <- granger_test(sv, "Nose_x_Central_Sitar", "Nose_x_Central_Tabla", lag = 3/25)
autoplot(g, splicing_df)

Autoplot a SpectralDensityView S3 object

Description

Autoplot a SpectralDensityView S3 object

Usage

## S3 method for class 'SpectralDensityView'
autoplot(object, period_range = c(0, 10), colour = "blue", ...)

Arguments

Value

a ggplot object.

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
sd1 <- spectral_density(pv, columns = c("LElbow_x", "LEye_x"), spans = 5)
autoplot(sd1)

fv <- apply_filter_sgolay(pv, data_points = c("LElbow", "LEye"), n = 19, p = 4)
sd2 <- spectral_density(fv, data_points = c("LElbow", "LEye"), spans = c(3, 3))
autoplot(sd2)

Calculate mean average cross power over splices using a splicing table

Description

Randomly generates splices from a splicing table and calculates average cross power for each segment and splice. Calculates the mean average cross power over the random splices for each segment and period. Compares with the average cross power for the original splice.

Usage

ave_cross_power_over_splices(
  jv,
  splicing_df,
  num_splices,
  columns,
  sampling_type = "offset",
  rejection_list = list(),
  include_original = TRUE,
  show_plot = TRUE
)

Arguments

Value

data.frame of splice segments and their average cross power.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)

d <- get_duration_annotation_data(r)
splicing_tabla_solo_df <- splice_time(d,
  expr = "Tier == 'INTERACTION' & Comments == 'Mutual look and smile'")

# Only do the first splice for sample data
mean_ave_cross_power_df <- ave_cross_power_over_splices(jv,
  splicing_tabla_solo_df[1,], num_splices = 10,
  columns = c('Nose_x_Central_Sitar', 'Nose_y_Central_Sitar'), show_plot = TRUE)

Get the average cross power on each segment in a SplicedView

Description

Get the average cross power on each segment in a SplicedView

Usage

ave_cross_power_spliceview(
  sv,
  columns,
  colour = "blue",
  segments = NULL,
  show_plot = FALSE,
  ...
)

Arguments

Value

data.frame with columns containing Average Cross Power for each segment.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
d1 <- get_duration_annotation_data(r)
# only one relevant section for sample data
splicing_smile_df <- splice_time(d1, tier ='INTERACTION',
  comments = 'Mutual look and smile')

fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)
sv_duration_smile <- get_spliced_view(jv, splicing_df = splicing_smile_df)
ave_cross_power_smile <- ave_cross_power_spliceview(
  sv_duration_smile, columns = c("Nose_x_Central_Sitar", "Nose_y_Central_Sitar"), show_plot = TRUE)
head(ave_cross_power_smile)

Calculate mean average power over splices using a splicing table

Description

Randomly generates splices from a splicing table and calculates average power for each segment and splice. Calculates the mean average power over the random splices for each segment and period. Compares with the average power for the original splice.

Usage

ave_power_over_splices(
  jv,
  splicing_df,
  num_splices,
  column,
  sampling_type = "offset",
  rejection_list = list(),
  include_original = TRUE,
  show_plot = TRUE
)

Arguments

Value

data.frame of splice segments and their average power.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)

d <- get_duration_annotation_data(r)
splicing_tabla_solo_df <- splice_time(d,
  expr = "Tier == 'INTERACTION' & Comments == 'Mutual look and smile'")

# Only do the first splice for sample data
mean_ave_power_df <- ave_power_over_splices(jv, splicing_tabla_solo_df[1,], num_splices = 10,
column = 'Nose_x_Central_Sitar', show_plot = TRUE)

Get the average power on each segment in a SplicedView

Description

Get the average power on each segment in a SplicedView

Usage

ave_power_spliceview(
  sv,
  column,
  colour = "blue",
  segments = NULL,
  show_plot = FALSE,
  ...
)

Arguments

Value

data.frame with columns containing Average Power for each segment.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
d1 <- get_duration_annotation_data(r)
# only one relevant section for sample data
splicing_smile_df <- splice_time(d1, tier ='INTERACTION',
  comments = 'Mutual look and smile')

fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)
sv_duration_smile <- get_spliced_view(jv, splicing_df = splicing_smile_df)
ave_power_smile <- ave_power_spliceview(sv_duration_smile,
  column = "Nose_x_Central_Sitar", show_plot=TRUE)
head(ave_power_smile)

Calculate average cross power distribution using a splicing table

Description

Calculate average cross power distribution using a splicing table

Usage

calculate_ave_cross_power1(
  jv,
  splicing_df,
  splice_name,
  num_segment_samples,
  columns,
  show_plot = TRUE
)

Arguments

Value

a data frame: containing average cross power on the spliced JoinedView.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, data_points = 'Nose', n = 41, p = 3)
jv <- get_joined_view(fv_list)
splicing_df <- splice_time(list(a = c(0, 5), b = c(10, 15)))
output_dfr <- calculate_ave_cross_power1(jv, splicing_df, 'Splice', 10,
  c('Nose_x_Central_Tabla', 'Nose_y_Central_Tabla'))

Calculate average power distribution using a splicing table

Description

Calculate average power distribution using a splicing table

Usage

calculate_ave_power1(
  jv,
  splicing_df,
  splice_name,
  num_segment_samples,
  column,
  show_plot = TRUE
)

Arguments

Value

a data frame: containing average power on the spliced JoinedView.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, data_points = 'Nose', n = 41, p = 3)
jv <- get_joined_view(fv_list)
splicing_df <- splice_time(list(a = c(0, 5), b = c(10, 15)))
output_dfr <- calculate_ave_power1(jv, splicing_df, 'Splice', 10, 'Nose_x_Central_Tabla')

Clip a splice so segments are of fixed duration

Description

Clip a splice so segments are of fixed duration

Usage

clip_splice(splice_dfr, duration, location = "middle")

Arguments

Value

a Splice object.

See Also

Other splicing functions: get_spliced_view(), is_splice_overlapping(), merge_splice(), splice_time.Duration(), splice_time.Metre(), splice_time.OnsetsDifference(), splice_time.View(), splice_time.list(), splice_time(), split.SplicedView()

Examples

l <- list(a = c(10, 20), b = c(30, 40),c = c(50, 55))
splice_dfr <- splice_time(l)
clip_splice(splice_dfr, duration = 1)
clip_splice(splice_dfr, duration = 6)
clip_splice(splice_dfr, duration = 1, location = 'beginning')
clip_splice(splice_dfr, duration = 10, location = 'beginning')
clip_splice(splice_dfr, duration = 1, location = 'end')
clip_splice(splice_dfr, duration = 10, location = 'end')

Compare average cross power distribution using a splicing table

Description

Compare average cross power distribution using a splicing table

Usage

compare_ave_cross_power1(
  jv,
  splicing_df,
  splice_name,
  num_segment_samples,
  num_splice_samples,
  columns,
  sampling_type = "offset",
  rejection_list = list(),
  show_plot = TRUE
)

Arguments

Value

list of two data frames: one containing average cross power on the first splice and the other containing the average cross power on randomly generated splices.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, data_points = 'Nose', n = 41, p = 3)
jv <- get_joined_view(fv_list)
splicing_df <- splice_time(list(a = c(0, 5), b = c(10, 15)))
output_list <- compare_ave_cross_power1(jv, splicing_df, 'Random Splices', 5, 5,
c('Nose_x_Central_Tabla', 'Nose_y_Central_Tabla'))

Compare average power distribution using a splicing table

Description

Compare average power distribution using a splicing table

Usage

compare_ave_power1(
  jv,
  splicing_df,
  splice_name,
  num_segment_samples,
  num_splice_samples,
  column,
  sampling_type = "offset",
  rejection_list = list(),
  show_plot = TRUE
)

Arguments

Value

list of two data frames: one containing average power on the first splice and the other containing the average power on randomly generated splices.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, data_points = 'Nose', n = 41, p = 3)
jv <- get_joined_view(fv_list)
splicing_df <- splice_time(list(a = c(0, 5), b = c(10, 15)))
output_list <- compare_ave_power1(jv, splicing_df, 'Random Splices', 5, 5, 'Nose_x_Central_Tabla')

Compare the average cross power distribution of two SplicedViews using sampling on each segment

Description

Compare the average cross power distribution of two SplicedViews using sampling on each segment

Usage

compare_avg_cross_power2(
  sv1,
  sv2,
  name1,
  name2,
  num_samples,
  columns,
  show_plot = TRUE
)

Arguments

Value

list of two data.frames containing the sampled data.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
d1 <- get_duration_annotation_data(r)
fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)

# only one relevant section for sample data
splicing_smile_df <- splice_time(d1, tier ='INTERACTION',
  comments = 'Mutual look and smile')
sv_duration_smile <- get_spliced_view(jv, splicing_df = splicing_smile_df)

splicing_alap_df <- splice_time(
  d1, tier = 'FORM', comments = 'Alap'
)
sv_duration_alap <- get_spliced_view(jv, splicing_df = splicing_alap_df)

sample_list <- compare_avg_cross_power2(
sv_duration_smile, sv_duration_alap, 'Smile', 'Alap', num_samples = 100,
  columns = c("Nose_x_Central_Sitar", "Nose_y_Central_Sitar"))

Compare the average power distribution of two SplicedViews using sampling on each segment

Description

Compare the average power distribution of two SplicedViews using sampling on each segment

Usage

compare_avg_power2(
  sv1,
  sv2,
  name1,
  name2,
  num_samples,
  column,
  show_plot = TRUE
)

Arguments

Value

list of two data.frames containing the sampled data.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
d1 <- get_duration_annotation_data(r)
fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)

# only one relevant section for sample data
splicing_smile_df <- splice_time(d1, tier ='INTERACTION',
  comments = 'Mutual look and smile')
sv_duration_smile <- get_spliced_view(jv, splicing_df = splicing_smile_df)

splicing_alap_df <- splice_time(
  d1, tier = 'FORM', comments = 'Alap'
)
sv_duration_alap <- get_spliced_view(jv, splicing_df = splicing_alap_df)

sample_list <- compare_avg_power2(
sv_duration_smile, sv_duration_alap, 'Smile', 'Alap', num_samples = 100,
  column = "Nose_x_Central_Sitar")

Get onset differences

Description

Calculates the difference in onset times for each instrument pair in milli-seconds.

Usage

difference_onsets(onset_obj, instruments, expr = NULL, splicing_dfr = NULL)

Arguments

Value

OnsetsDifference object.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r1 <- get_sample_recording()
o1 <- get_onsets_selected_data(r1)
head(difference_onsets(o1, instruments = c('Inst', 'Tabla')))
head(difference_onsets(o1, instruments = c('Inst', 'Tabla'), expr = 'Matra == 3'))

Distribution plot of a view object

Description

Distribution plot of a view object

Usage

distribution_dp(obj, maxpts = 50000, alpha = 0.1, ...)

Arguments

Value

a ggplot object.

Examples

r1 <- get_sample_recording()
rv1 <- get_raw_view(r1, "Central", "", "Sitar")
pv1 <- get_processed_view(rv1)
dp <- c("LWrist", "RWrist", "LElbow", "RElbow", "LEye", "REye", "Neck", "MidHip")
fv1 <- apply_filter_sgolay(pv1, data_point = dp, n = 41, p = 4)
distribution_dp(fv1)

Get the data points held in a view

Description

Get the data points held in a view

Usage

get_data_points(obj)

Arguments

Value

character vector of body parts.

See Also

Other data functions: apply_filter_sgolay(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
get_data_points(rv)

Get duration annotation data

Description

Get duration annotation data

Usage

get_duration_annotation_data(recording, filetype = "rda", verbose = FALSE)

Arguments

Value

list of data.frames.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
df <- get_duration_annotation_data(r)

Get Feature Data

Description

Output from new analysis process that generates data at the same sample rate as the video data. The user is responsible for ensuring that this data is continuous before using this function.

Usage

get_feature_data(
  recording,
  vid,
  direct,
  inst,
  interpolate_data = FALSE,
  folder_out = tempdir(),
  save_output = FALSE,
  filetype = "rda",
  verbose = FALSE
)

Arguments

Value

a FilteredView object.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
fd <- get_feature_data(r, "Central" ,"", "Sitar")
fv_list <- get_filtered_views(r, 'LEar', n = 41, p =3)
fv_list$Feature <- fd
jv <- get_joined_view(fv_list)
get_data_points(jv)
autoplot(jv)

Get filtered views

Description

Get filtered views

Usage

get_filtered_views(r, data_points, n, p, filetype = "rda")

Arguments

Value

list of FilteredView objects.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, "Nose", n = 41, p = 3)
plot(fv_list$Central_Tabla)

Get Granger Causality interactions

Description

Get Granger Causality interactions

Usage

get_granger_interactions(
  sv,
  columns,
  cond_column = "",
  sig_level = 0.05,
  lag = 1,
  granger_fn = ms_grangertest2
)

Arguments

Value

GrangerInteraction object

See Also

Other Granger Causality: autoplot.GrangerTime(), granger_test(), map_to_granger_test(), ms_condgrangertest(), ms_grangertest1(), ms_grangertest2(), plot.GrangerInteraction(), plot_influence_diagram()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, "Nose", n = 41, p = 3)
jv_sub <- get_joined_view(fv_list)
l <- list(a = c(0, 300), b = c(300, 600), c = c(600, 900))
splicing_df <- splice_time(l)
sv <- get_spliced_view(jv_sub, splicing_df)
g <- get_granger_interactions(sv, c("Nose_x_Central_Sitar", "Nose_x_Central_Tabla"), lag = 1/25)
print(g)

Get joined view from multiple views from the same recording

Description

Get joined view from multiple views from the same recording

Usage

get_joined_view(l, folder_out = "Joined", save_output = FALSE)

Arguments

Value

JoinedView object

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
rv_list <- get_raw_views(r)
jv <- get_joined_view(rv_list)
plot(jv, columns = c("LEar_x_Central_Sitar", "LEar_x_Central_Tabla"), yax.flip=TRUE)

Get periods locally maximal average power

Description

Get periods locally maximal average power

Usage

get_local_max_average_power(obj, v)

Arguments

Value

data.frame of Period and Local Maxima.

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), plot_average_coherency(), plot_average_power(), plot_cross_spectrum(), plot_cwt_energy(), plot_phase_difference(), plot_power_spectrum(), plot_roll_resultant_length(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
pv1 <- subset(pv, Time >= 10)
w <- analyze_wavelet(pv1, "Nose_x")
plot_average_power(w, pv1)
get_local_max_average_power(w, pv1)

Get metre files

Description

Get metre files

Usage

get_metre_data(recording, filetype = "rda", verbose = FALSE)

Arguments

Value

list of data.frames.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
m <- get_metre_data(r)

Get onsets selected files

Description

Get onsets selected files

Usage

get_onsets_selected_data(
  recording,
  tactus = "Matra",
  filetype = "rda",
  verbose = FALSE
)

Arguments

Value

list of data.frames

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
o <- get_onsets_selected_data(r)

Get movementsync recording from OSF

Description

Get movementsync recording from OSF

Usage

get_osf_recordings(
  stems = c("NIR_ABh_Puriya", "NIRP1_VS_Hams", "NIRP1_MAK_Jaun", "Gagaku_5_Juha",
    "NIR_DBh_Malhar"),
  to_dir = tempdir(),
  overwrite = FALSE
)

Arguments

Value

invisible vector of downloaded CSV file names.

Examples

get_osf_recordings()

Get processed view from Pose video data

Description

Normalises and interpolates missing data in the view.

Usage

get_processed_view(
  rv,
  folder_out = tempdir(),
  save_output = FALSE,
  verbose = FALSE
)

Arguments

Value

a ProcessedView object.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)

Get processed views

Description

Get processed views

Usage

get_processed_views(r, data_points, filetype = "rda")

Arguments

Value

list of ProcessedView objects.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
pv_list <- get_processed_views(r)
plot(pv_list$Central_Tabla)

Creates time reference and displacement from raw csv optflow data

Description

Used to load OptFlow data.

Usage

get_raw_optflow_view(
  recording,
  vid,
  direct,
  inst,
  folder_out = tempdir(),
  save_output = FALSE,
  filetype = "rda",
  verbose = FALSE
)

Arguments

Value

an OptFlowView object.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_views(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_recording("NIR_ABh_Puriya", fps = 25)
rov <- get_raw_optflow_view(r, "Central" ,"", "Sitar")
pov <- get_processed_view(rov)
fv1 <- apply_filter_sgolay(pov, c("Head"), n=19, p=4)
autoplot(fv1)

Get view from Pose video data

Description

Creates time reference and displacement from raw csv data for the view.

Usage

get_raw_view(
  recording,
  vid,
  direct,
  inst,
  out_folder = tempdir(),
  save_output = FALSE,
  filetype = "rda",
  verbose = FALSE
)

Arguments

Value

a RawView object.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
v <- get_raw_view(r, "Central", "", "Sitar")

Get Pose views from a recording

Description

Get Pose views from a recording

Usage

get_raw_views(recording, filetype = "rda")

Arguments

Value

named list of views

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_view(), get_recording(), get_sample_recording()

Examples

r <- get_sample_recording()
v_list <- get_raw_views(r)

Get a meta-data recording object

Description

Get a meta-data recording object

Usage

get_recording(
  stem,
  fps,
  folder_in = "data",
  path = system.file(package = "movementsync"),
  filetype = "csv",
  verbose = FALSE
)

Arguments

Value

a Recording object.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_sample_recording()

Examples

# Get the details of one recording
r <- get_recording("NIR_ABh_Puriya", fps=25)

Get sample meta-data recording object

Description

Get sample meta-data recording object

Usage

get_sample_recording(stem = "NIR_ABh_Puriya")

Arguments

Value

a Recording object.

See Also

Other data functions: apply_filter_sgolay(), get_data_points(), get_duration_annotation_data(), get_feature_data(), get_filtered_views(), get_joined_view(), get_metre_data(), get_onsets_selected_data(), get_processed_views(), get_processed_view(), get_raw_optflow_view(), get_raw_views(), get_raw_view(), get_recording()

Examples

r <- get_sample_recording()

Get spliced view from view object

Description

Get spliced view from view object

Usage

get_spliced_view(v, splicing_df)

Arguments

Value

a SplicedView object.

See Also

Other splicing functions: clip_splice(), is_splice_overlapping(), merge_splice(), splice_time.Duration(), splice_time.Metre(), splice_time.OnsetsDifference(), splice_time.View(), splice_time.list(), splice_time(), split.SplicedView()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
l <- list(a = c(0, 10), b = c(10, 20), c = c(20, 30))
splicing_df <- splice_time(l)
sv <- get_spliced_view(rv, splicing_df)

Granger causality tests applied to a SplicedView

Description

Granger causality tests applied to a SplicedView

Usage

granger_test(
  obj,
  var1,
  var2,
  var3 = "",
  lag = 1,
  granger_fn = ms_grangertest2,
  cond_granger_fn = ms_condgrangertest
)

Arguments

Value

GrangerTime object

See Also

Other Granger Causality: autoplot.GrangerTime(), get_granger_interactions(), map_to_granger_test(), ms_condgrangertest(), ms_grangertest1(), ms_grangertest2(), plot.GrangerInteraction(), plot_influence_diagram()

Examples

r1 <- get_sample_recording()
rv_list <- get_raw_views(r1)
pv_list <- lapply(rv_list, get_processed_view)
get_data_points(pv_list$Central_Sitar)
fv_list <- lapply(pv_list, apply_filter_sgolay, data_points = "Nose", n = 41, p = 3)
jv_sub <- get_joined_view(fv_list)
splicing_df <- splice_time(jv_sub, win_size = 5, step_size = 0.5)
sv <- get_spliced_view(jv_sub, splicing_df)
granger_test(sv, "Nose_x_Central_Sitar", "Nose_x_Central_Tabla", lag = 1/25)
granger_test(sv, "Nose_x_Central_Sitar", "Nose_x_Central_Tabla", "Nose_y_Central_Tabla", lag = 1/25)

Checks if splicing data.frames overlap

Description

Checks if splicing data.frames overlap

Usage

is_splice_overlapping(...)

Arguments

Value

logical

See Also

Other splicing functions: clip_splice(), get_spliced_view(), merge_splice(), splice_time.Duration(), splice_time.Metre(), splice_time.OnsetsDifference(), splice_time.View(), splice_time.list(), splice_time(), split.SplicedView()

Examples

l1 <- list(a=c(1, 10), a = c(20, 30), b = c(30, 40))
dfr1 <- splice_time(l1)
l2 <- list(a=c(10, 15), b = c(15, 25))
dfr2 <- splice_time(l2)
is_splice_overlapping(dfr1, dfr2)

List available recordings for movementsync from OSF

Description

List available recordings for movementsync from OSF

Usage

list_osf_recordings()

Value

character vector of stem names

Examples

list_osf_recordings()

Map duration object comments to a Granger Test object

Description

Map duration object comments to a Granger Test object

Usage

map_to_granger_test(d, g, influence1, influence2)

Arguments

Value

modified Duration object

See Also

Other Granger Causality: autoplot.GrangerTime(), get_granger_interactions(), granger_test(), ms_condgrangertest(), ms_grangertest1(), ms_grangertest2(), plot.GrangerInteraction(), plot_influence_diagram()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv_sub <- get_joined_view(fv_list)
splicing_df <- splice_time(jv_sub, win_size = 5, step_size = 0.5)
sv <- get_spliced_view(jv_sub, splicing_df)
g <- granger_test(sv, "Nose_x_Central_Sitar", "Nose_x_Central_Tabla", lag = 1/25)
d <- get_duration_annotation_data(r)
map_to_granger_test(d, g, "Influence T>S", "Influence S>T")

Merge splices together using set operations

Description

Merge splices together using set operations

Usage

merge_splice(..., operation)

Arguments

Value

a Splice object.

See Also

Other splicing functions: clip_splice(), get_spliced_view(), is_splice_overlapping(), splice_time.Duration(), splice_time.Metre(), splice_time.OnsetsDifference(), splice_time.View(), splice_time.list(), splice_time(), split.SplicedView()

Examples

l1 <- list(a1 = c(100, 200), a2 = c(250, 300), a3 = c(400, 550), a4 = c(600, 650))
split1_dfr <- splice_time(l1)
split1_dfr

l2 <- list(b1 = c(150, 275), b2 = c(610, 640))
split2_dfr <- splice_time(l2)
split2_dfr

l3 <- list(c1 = c(275, 325), c2 = c(600, 675), c3 = c(700, 725))
split3_dfr <- splice_time(l3)
split3_dfr

merge_splice(x = split1_dfr, y = split2_dfr, z = split3_dfr, operation = 'union')
merge_splice(x = split1_dfr, y = split2_dfr, z = split3_dfr, operation = 'intersection')

Motion gram of a view object

Description

Motion gram of a view object

Usage

motion_gram(obj, maxpts = 10000, alpha = 0.5, ...)

Arguments

Value

a gtable object.

Examples

r1 <- get_sample_recording()
rv1 <- get_raw_view(r1, "Central", "", "Sitar")
pv1 <- get_processed_view(rv1)
dp <- c("LWrist", "RWrist", "LElbow", "RElbow", "LEye", "REye", "MidHip")
fv1 <- apply_filter_sgolay(pv1, data_point = dp, n = 41, p = 4)
sub_fv1 <- subset(fv1, Time >= 0 & Time <= 20, dp, by = 2)
motion_gram(sub_fv1)

Test for Conditional Granger Causality

Description

Faster implementation of the vector version of lmtest::grangertest() with conditioning on the history of a third variable. The function assumes time series always have the same start date and periodicity, which is true for the data in this package.

Usage

ms_condgrangertest(x, y, z, order = 1, na.action = stats::na.omit, ...)

Arguments

Value

Anova object

See Also

Other Granger Causality: autoplot.GrangerTime(), get_granger_interactions(), granger_test(), map_to_granger_test(), ms_grangertest1(), ms_grangertest2(), plot.GrangerInteraction(), plot_influence_diagram()

Examples

data(wages, package = "lmtest")
diff_wages <- diff(wages)

# Granger tests
lmtest::grangertest(diff_wages[, 'w'], diff_wages[, 'CPI'], order = 3)
ms_grangertest1(diff_wages[, 'w'], diff_wages[, 'CPI'], order = 3)
ms_grangertest2(diff_wages[, 'w'], diff_wages[, 'CPI'], order = 3)

ms_condgrangertest(diff_wages[, 'w'], diff_wages[, 'CPI'], diff_wages[, 'u'], order = 3)

Test for Granger Causality

Description

Faster implementation of the vector version of lmtest::grangertest() which uses a vectorised lag operation.

Usage

ms_grangertest1(x, y, order = 1, na.action = stats::na.omit, ...)

Arguments

Value

Anova object

See Also

Other Granger Causality: autoplot.GrangerTime(), get_granger_interactions(), granger_test(), map_to_granger_test(), ms_condgrangertest(), ms_grangertest2(), plot.GrangerInteraction(), plot_influence_diagram()

Examples

data(ChickEgg, package = "lmtest")
ms_grangertest1(ChickEgg, order = 3)

Test for Granger Causality

Description

Faster implementation of the vector version of lmtest::grangertest(). The function assumes time series always have the same start date and periodicity, which is true for the data in this package.

Usage

ms_grangertest2(x, y, order = 1, na.action = stats::na.omit, ...)

Arguments

Value

Anova object

See Also

Other Granger Causality: autoplot.GrangerTime(), get_granger_interactions(), granger_test(), map_to_granger_test(), ms_condgrangertest(), ms_grangertest1(), plot.GrangerInteraction(), plot_influence_diagram()

Examples

data(ChickEgg, package = "lmtest")
ms_grangertest2(ChickEgg, order = 3)

Opens movementsync data home page at OSF

Description

Opens movementsync data home page at OSF

Usage

open_movementsync_data()

Value

No return value, opens a browser on a specific OSF page

Plot a Duration S3 object

Description

Plot a Duration S3 object

Usage

## S3 method for class 'Duration'
plot(x, ...)

Arguments

Value

a plot object with durations.

Examples

r <- get_sample_recording()
d <- get_duration_annotation_data(r)
plot(d)

Plot network diagram of Granger Causalities

Description

Plot network diagram of Granger Causalities

Usage

## S3 method for class 'GrangerInteraction'
plot(x, mfrow = NULL, mar = c(1, 1, 1, 1), oma = c(1, 1, 1, 1), ...)

Arguments

Value

data.frame of P-Values

See Also

Other Granger Causality: autoplot.GrangerTime(), get_granger_interactions(), granger_test(), map_to_granger_test(), ms_condgrangertest(), ms_grangertest1(), ms_grangertest2(), plot_influence_diagram()

Examples

r <- get_recording("NIR_ABh_Puriya", fps = 25)
fv_list <- get_filtered_views(r, "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)
jv <- subset(jv, Time <= 5*60)
l <- list(a = c(0, 100), b = c(100, 200), c = c(200, 300))
splicing_df <- splice_time(l)
sv <- get_spliced_view(jv, splicing_df)
gi <- get_granger_interactions(sv, c("Nose_x_Central_Sitar", "Nose_x_Central_Tabla"), lag = 1/25)
print(gi)

Plot a Metre S3 object

Description

Plot a Metre S3 object

Usage

## S3 method for class 'Metre'
plot(x, ...)

Arguments

Value

a plot object with metre.

Examples

r <- get_sample_recording()
m <- get_metre_data(r)
plot(m)

Plot a OnsetsSelected S3 object

Description

Plot a OnsetsSelected S3 object

Usage

## S3 method for class 'OnsetsSelected'
plot(x, instrument = "Inst", tactus = "Matra", ...)

Arguments

Value

Return an 'OnsetsSelected' object.

Examples

r <- get_sample_recording()
o <- get_onsets_selected_data(r)
plot(o)

Plot a View S3 object

Description

Plot a View S3 object

Usage

## S3 method for class 'View'
plot(x, columns = NULL, maxpts = 1000, ...)

Arguments

Value

a plot object.

Examples

r <- get_sample_recording()
v <- get_raw_view(r, "Central", "", "Sitar")
plot(v, columns = "LEar_x")

Plot average coherency of a coherency object

Description

Plot average coherency of a coherency object

Usage

plot_average_coherency(obj, view, ...)

Arguments

Value

a ggplot object.

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), get_local_max_average_power(), plot_average_power(), plot_cross_spectrum(), plot_cwt_energy(), plot_phase_difference(), plot_power_spectrum(), plot_roll_resultant_length(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
co <- analyze_coherency(pv, columns = c("Nose_x", "Nose_y"))
plot_average_coherency(co, pv)

Plot average power of a wavelet object

Description

Plot average power of a wavelet object

Usage

plot_average_power(obj, view, ...)

Arguments

Value

a ggplot object.

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), get_local_max_average_power(), plot_average_coherency(), plot_cross_spectrum(), plot_cwt_energy(), plot_phase_difference(), plot_power_spectrum(), plot_roll_resultant_length(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
pv1 <- subset(pv, Time >= 10)
w <- analyze_wavelet(pv1, "Nose_x")
plot_average_power(w, pv1)
w <- analyze_wavelet(pv1, "Nose_y")
plot_average_power(w, pv1)

Plot a coherency of a wavelet object

Description

Plot a coherency of a wavelet object

Usage

plot_cross_spectrum(obj, view, ...)

plot_coherence(obj, view, ...)

Arguments

Value

a list of class ⁠graphical parameters⁠,

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), get_local_max_average_power(), plot_average_coherency(), plot_average_power(), plot_cwt_energy(), plot_phase_difference(), plot_power_spectrum(), plot_roll_resultant_length(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
pv1 <- subset(pv, Time >= 10)
co <- analyze_coherency(pv1, c("Nose_x", "Nose_y"))
plot_cross_spectrum(co, pv1)
plot_coherence(co, pv1)

Plot cross wavelet energy of a wavelet object

Description

Plot cross wavelet energy of a wavelet object

Usage

plot_cwt_energy(obj, view)

Arguments

Value

a ggplot object.

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), get_local_max_average_power(), plot_average_coherency(), plot_average_power(), plot_cross_spectrum(), plot_phase_difference(), plot_power_spectrum(), plot_roll_resultant_length(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
co <- analyze_coherency(pv, columns = c("Nose_x", "Nose_y"))
plot_cwt_energy(co, pv)

Plot a set of data points over time

Description

Plot a set of data points over time

Usage

plot_history_xy(obj, maxpts = 10000)

Arguments

Value

a ggplot object.

Examples

r1 <- get_sample_recording()
rv1 <- get_raw_view(r1, "Central", "", "Sitar")
pv1 <- get_processed_view(rv1)
fv1 <- apply_filter_sgolay(pv1, data_points = c("LElbow", "RElbow"), n = 41, p = 3)
sub_fv1 <- subset(fv1, Time >= 0 & Time <= 100, by = 10)
plot_history_xy(sub_fv1)

Plot influence diagram from a GrangerTest object

Description

Arrows show causality (influencing) direction.

Usage

plot_influence_diagram(obj, splicing_df, two_arrows = TRUE, lev_sig = 0.05)

Arguments

Details

By default two_arrows is TRUE and an influencing arrow is drawn for each significant p-value. If two_arrows is FALSE and one of the p-values is signficant then -log10(p_value) difference is plotted i.e

Value

ggplot object

See Also

Other Granger Causality: autoplot.GrangerTime(), get_granger_interactions(), granger_test(), map_to_granger_test(), ms_condgrangertest(), ms_grangertest1(), ms_grangertest2(), plot.GrangerInteraction()

Examples

r1 <- get_sample_recording()
fv_list <- get_filtered_views(r1, data_points = "Nose", n = 41, p = 3)
jv_sub <- get_joined_view(fv_list)
splicing_df <- splice_time(jv_sub, win_size = 3, step_size = 0.5)
sv <- get_spliced_view(jv_sub, splicing_df)
g <- granger_test(sv, "Nose_x_Central_Sitar", "Nose_x_Central_Tabla", lag = 3/25)

plot_influence_diagram(g, splicing_df)
plot_influence_diagram(g, splicing_df, two_arrows = TRUE)

d1 <- get_duration_annotation_data(r1)
plot_influence_diagram(g, splicing_df) +
autolayer(d1, expr = (Tier == "Influence S>T" | Tier == "Influence T>S") & Out <= 60,
          fill_col = "Tier")

Plot a coherency of a wavelet object

Description

Plot a coherency of a wavelet object

Usage

plot_phase_difference(obj, view, ...)

Arguments

Value

a list of class ⁠graphical parameters⁠

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), get_local_max_average_power(), plot_average_coherency(), plot_average_power(), plot_cross_spectrum(), plot_cwt_energy(), plot_power_spectrum(), plot_roll_resultant_length(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
pv1 <- subset(pv, Time >= 10 & Time <= 20)
co <- analyze_coherency(pv1, c("Nose_x", "Nose_y"))
plot_phase_difference(co, pv1)

Plot a power spectrum of a wavelet object

Description

Plot a power spectrum of a wavelet object

Usage

plot_power_spectrum(obj, view, ...)

Arguments

Value

a list of class ⁠graphical parameters⁠.

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), get_local_max_average_power(), plot_average_coherency(), plot_average_power(), plot_cross_spectrum(), plot_cwt_energy(), plot_phase_difference(), plot_roll_resultant_length(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
pv1 <- subset(pv, Time >= 30)
w <- analyze_wavelet(pv1, "Nose_y")
plot_power_spectrum(w, pv1)
w <- analyze_wavelet(pv1, "Nose_y", lowerPeriod = 0.01, upperPeriod = 10)
plot_power_spectrum(w, pv1)

Plot windowed resultant length

Description

Plot windowed resultant length

Usage

plot_roll_resultant_length(
  obj,
  window_duration = 1,
  smooth = FALSE,
  by = 1,
  ref_lines = c(W = 0.7, M = 0.85, H = 0.95),
  align = "right",
  na.rm = TRUE
)

Arguments

Value

a ggplot object.

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), get_local_max_average_power(), plot_average_coherency(), plot_average_power(), plot_cross_spectrum(), plot_cwt_energy(), plot_phase_difference(), plot_power_spectrum(), plot_sel_phases(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
co <- analyze_coherency(pv, columns = c("Nose_x", "Nose_y"))
sp <- plot_sel_phases(co, pv, sel.period = 0.64)
plot_roll_resultant_length(sp, ref_lines = c(H = 0.9998))

Comparison plot of phases of a coherency object

Description

Comparison plot of phases of a coherency object

Usage

plot_sel_phases(
  obj,
  view,
  sel.period = NULL,
  sel.upper = NULL,
  sel.lower = NULL,
  ...
)

Arguments

Value

an object of class sel.phases.

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), get_local_max_average_power(), plot_average_coherency(), plot_average_power(), plot_cross_spectrum(), plot_cwt_energy(), plot_phase_difference(), plot_power_spectrum(), plot_roll_resultant_length(), plot_wt_energy()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
co <- analyze_coherency(pv, columns = c("Nose_x", "Nose_y"))
plot_cross_spectrum(co, pv)
plot_sel_phases(co, pv, sel.period = 0.64)
plot_sel_phases(co, pv, sel.lower = 0.6, sel.upper = 0.8)

Plot wavelet energy of a wavelet object

Description

Plot wavelet energy of a wavelet object

Usage

plot_wt_energy(obj, view)

Arguments

Value

a ggplot object.

See Also

Other wavelet functions: analyze_coherency(), analyze_wavelet(), get_local_max_average_power(), plot_average_coherency(), plot_average_power(), plot_cross_spectrum(), plot_cwt_energy(), plot_phase_difference(), plot_power_spectrum(), plot_roll_resultant_length(), plot_sel_phases()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
pv1 <- subset(pv, Time >= 10)
w <- analyze_wavelet(pv1, "Nose_x")
plot_wt_energy(w, pv1)

Apply function to SplicedView and pull out element from output

Description

Apply function to SplicedView and pull out element from output

Usage

pull_segment_spliceview(sv, FUN, element, ...)

Arguments

Value

list with output and input fields.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), sample_gap_splice(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r <- get_sample_recording()
d1 <- get_duration_annotation_data(r)
# only one relevant section for sample data
splicing_smile_df <- splice_time(d1, tier ='INTERACTION',
  comments = 'Mutual look and smile')

fv_list <- get_filtered_views(r, data_points = "Nose", n = 41, p = 3)
jv <- get_joined_view(fv_list)
sv_duration_smile <- get_spliced_view(jv, splicing_df = splicing_smile_df)
pull_segment_spliceview(sv_duration_smile, FUN = analyze_wavelet,
                        column = "Nose_x_Central_Sitar", element = 'Power')

Randomly create matching segments from a splicing table without overlaps

Description

Works by randomly varying the gaps between segments assuming that the gap number follow a Poisson process with rate given by the average sample gap length in the input splice. Durations of segments remain the same.

Usage

sample_gap_splice(splicing_dfr, v, num_splices, rejection_list = list())

Arguments

Details

Uses rejection sampling to avoid overlaps with the input segments and additional segments from a list of splices.

Value

list of splicing data.frames.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_offset_splice(), summary_onsets(), visualise_sample_splices()

Examples

r1 <- get_sample_recording()
d1 <- get_duration_annotation_data(r1)
rv1 <- get_raw_view(r1, "Central", "", "Sitar")
splicing_df <- splice_time(d1, tier ='INTERACTION', comments = 'Mutual look and smile')
# Only first segment relevant for sample data
x <- sample_gap_splice(splicing_df[1,], rv1, num_splices = 10)

Randomly create matching segments from a splicing table without overlaps

Description

Works by adding a random offset to each start time in the splice. Uses rejection sampling to avoid overlaps with the input segments and additional segments from a list of splices.

Usage

sample_offset_splice(splicing_dfr, v, num_splices, rejection_list = list())

Arguments

Value

list of splicing data.frames.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), summary_onsets(), visualise_sample_splices()

Examples

r1 <- get_sample_recording()
d1 <- get_duration_annotation_data(r1)
rv1 <- get_raw_view(r1, "Central", "", "Sitar")
splicing_df <- splice_time(d1, tier ='INTERACTION', comments = 'Mutual look and smile')
# Only first segment relevant for sample data
x <- sample_offset_splice(splicing_df[1,], rv1, num_splices = 100)

Sample the time line from a list of Views

Description

Sample the time line from a list of Views

Usage

sample_time_spliced_views(
  ...,
  num_samples,
  replace = FALSE,
  na.action = stats::na.pass
)

Arguments

Value

a list of SplitView object or a SplitView object

Examples

r1 <- get_sample_recording()
fv1_list <- get_filtered_views(r1, data_points = "Nose", n = 41, p = 3)
jv1 <- get_joined_view(fv1_list)
l <- list(a=c(1, 2), b = c(2, 3))
splicing_df <- splice_time(l)
sv <- get_spliced_view(jv1, splicing_df = splicing_df)
autoplot(sv)
sv_new <- sample_time_spliced_views(sv, num_samples = 10, replace = FALSE)
autoplot(sv_new)
sv_new <- sample_time_spliced_views(sv, num_samples = 10, replace = TRUE)
autoplot(sv_new)
l <- list(a=c(1, 2), a = c(10, 20), b = c(30, 40))
splicing_df <- splice_time(l)
sv <- get_spliced_view(jv1, splicing_df = splicing_df)
sv_new <- sample_time_spliced_views(sv, num_samples = 20, replace = TRUE)
autoplot(sv_new)

Specgram Plot

Description

Specgram Plot

Usage

specgram_plot(obj, ...)

Arguments

Value

a ggplot object.

Examples

r <- get_recording("NIR_ABh_Puriya", fps = 25)
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
sub_pv <- subset(pv, Time >= 15 & Time <= 25, columns = c("RWrist_x", "RWrist_y"))
specgram_plot(sub_pv)
fv <- apply_filter_sgolay(pv, data_points = c("RWrist"), n = 11, p = 4)
sub_fv <- subset(fv, Time >= 15 & Time <= 25, columns = c("RWrist_x", "RWrist_y"))
specgram_plot(sub_fv)
specgram_plot(sub_fv, window = 200) + ggplot2::scale_fill_gradient(low = "white", high = "black")

Estimate the spectral density of data points

Description

Estimates the periodicity of data points in a View object.

Usage

spectral_density(view, columns = NULL, data_points = NULL, ...)

Arguments

Value

SpectralDensityView object.

Examples

r<-get_recording("NIR_ABh_Puriya", fps=25)
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
sd1 <- spectral_density(pv, columns = "LEar_x", spans = 5)

fv <- apply_filter_sgolay(pv, data_points = c("LEye"), n = 19, p = 4)
sd1 <- spectral_density(fv, data_points = c("LEye"), spans = 5)

S3 generic function to splice a timeline

Description

S3 generic function to splice a timeline

Usage

splice_time(x, ...)

Arguments

Value

a Splice object.

See Also

Other splicing functions: clip_splice(), get_spliced_view(), is_splice_overlapping(), merge_splice(), splice_time.Duration(), splice_time.Metre(), splice_time.OnsetsDifference(), splice_time.View(), splice_time.list(), split.SplicedView()

Generate spliced timeline using a Duration object

Description

Generate spliced timeline using a Duration object

Usage

## S3 method for class 'Duration'
splice_time(
  x,
  expr = NULL,
  make.unique = TRUE,
  tier = NULL,
  comments = NULL,
  ...
)

Arguments

Value

a Splice object.

See Also

Other splicing functions: clip_splice(), get_spliced_view(), is_splice_overlapping(), merge_splice(), splice_time.Metre(), splice_time.OnsetsDifference(), splice_time.View(), splice_time.list(), splice_time(), split.SplicedView()

Examples

r <- get_sample_recording()
d <- get_duration_annotation_data(r)
splice_time(d, tier = 'Event', comments = 'tabla solo')

Generate spliced timeline using a Metre object

Description

Generate spliced timeline using a Metre object

Usage

## S3 method for class 'Metre'
splice_time(
  x,
  window_duration = NULL,
  window_proportion = NULL,
  tactus = NULL,
  ...
)

Arguments

Value

a Splice object.

See Also

Other splicing functions: clip_splice(), get_spliced_view(), is_splice_overlapping(), merge_splice(), splice_time.Duration(), splice_time.OnsetsDifference(), splice_time.View(), splice_time.list(), splice_time(), split.SplicedView()

Examples

r <- get_sample_recording()
m <- get_metre_data(r)
splicing_df <- splice_time(m, window_duration = 1)
head(splicing_df)
splicing_df <- splice_time(m, window_proportion = 0.25)
head(splicing_df)

Generate spliced timeline using an OnsetsDifference object

Description

Generate spliced timeline using an OnsetsDifference object

Usage

## S3 method for class 'OnsetsDifference'
splice_time(x, window_duration, metres = NULL, make.unique = TRUE, ...)

Arguments

Value

a Splice object.

See Also

Other splicing functions: clip_splice(), get_spliced_view(), is_splice_overlapping(), merge_splice(), splice_time.Duration(), splice_time.Metre(), splice_time.View(), splice_time.list(), splice_time(), split.SplicedView()

Examples

r <- get_sample_recording()
o1 <- get_onsets_selected_data(r)
po1 <- difference_onsets(o1, instruments = c('Inst', 'Tabla'))
splicing_df <- splice_time(po1, window_duration = 1)
head(splicing_df)

Generate spliced timeline using a view

Description

Generate spliced timeline using a view

Usage

## S3 method for class 'View'
splice_time(x, win_size, step_size, ...)

Arguments

Value

a Splice object.

See Also

Other splicing functions: clip_splice(), get_spliced_view(), is_splice_overlapping(), merge_splice(), splice_time.Duration(), splice_time.Metre(), splice_time.OnsetsDifference(), splice_time.list(), splice_time(), split.SplicedView()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
df <- splice_time(rv, win_size = 3, step_size = 0.5)
head(df)

Generate spliced timeline using a list

Description

Generate spliced timeline using a list

Usage

## S3 method for class 'list'
splice_time(x, ...)

Arguments

Value

a Splice object.

See Also

Other splicing functions: clip_splice(), get_spliced_view(), is_splice_overlapping(), merge_splice(), splice_time.Duration(), splice_time.Metre(), splice_time.OnsetsDifference(), splice_time.View(), splice_time(), split.SplicedView()

Examples

l <- list(a = c(0, 10), b = c(10, 20), c = c(20, 30))
splice_time(l)

Get a list of Views from a SplicedView

Description

Get a list of Views from a SplicedView

Usage

## S3 method for class 'SplicedView'
split(x, f, drop, ...)

Arguments

Value

list of View objects.

See Also

Other splicing functions: clip_splice(), get_spliced_view(), is_splice_overlapping(), merge_splice(), splice_time.Duration(), splice_time.Metre(), splice_time.OnsetsDifference(), splice_time.View(), splice_time.list(), splice_time()

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
l <- list(a = c(0, 10), b = c(10, 20), c = c(20, 30))
splicing_df <- splice_time(l)
sv <- get_spliced_view(pv, splicing_df)
v_list <- split(sv)

Subset a View

Description

Simple time and column subsetting of views.

Usage

## S3 method for class 'View'
subset(x, expr = NULL, data_points = NULL, columns = NULL, by = NULL, ...)

Arguments

Value

a View object.

Examples

r <- get_sample_recording()
v <- get_raw_view(r, "Central", "", "Sitar")
vv <- subset(v, Time < 10, data_point = "Nose")
plot(vv)

Summarise Duration object

Description

Summarise Duration object

Usage

## S3 method for class 'Duration'
summary(object, ...)

Arguments

Value

data.frame

Examples

r <- get_sample_recording()
d <- get_duration_annotation_data(r)
head(summary(d))

Summarise Metre object

Description

Summarises the cycle length for each Metre.

Usage

## S3 method for class 'Metre'
summary(object, ...)

Arguments

Value

list of summaries.

Examples

r <- get_sample_recording()
m <- get_metre_data(r)
summary(m)

Summarise OnsetsSelected object

Description

Summarise OnsetsSelected object

Usage

## S3 method for class 'OnsetsSelected'
summary(object, ...)

Arguments

Value

list of summaries.

Examples

r <- get_sample_recording()
o <- get_onsets_selected_data(r)
summary(o)

Summarise Recording object

Description

Summarise Recording object

Usage

## S3 method for class 'Recording'
summary(object, ...)

Arguments

Value

list

Examples

r <- get_sample_recording()
summary(r)

Summarise a View object

Description

Summarise a View object

Usage

## S3 method for class 'View'
summary(object, ...)

Arguments

Value

summary of data.frame.

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
fv <- apply_filter_sgolay(pv, c("Nose", "RWrist", "LWrist"), n=19, p=4)
summary(rv)
summary(pv)
summary(fv)

Summarise an analyze.wavelet object

Description

Summarise an analyze.wavelet object

Usage

## S3 method for class 'analyze.wavelet'
summary(object, v, ...)

Arguments

Value

data.frame

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
w <- analyze_wavelet(pv, "Nose_x")
summary(w, pv)

Summarises a sel.phases object

Description

Summarises a sel.phases object

Usage

## S3 method for class 'sel.phases'
summary(object, na.rm = TRUE, ...)

Arguments

Value

list of Circular statistics.

Examples

r <- get_sample_recording()
rv <- get_raw_view(r, "Central", "", "Sitar")
pv <- get_processed_view(rv)
co <- analyze_coherency(pv, columns = c("Nose_x", "Nose_y"))
sp <- plot_sel_phases(co, pv, sel.period = NULL, sel.lower = 0.5, sel.upper = 0.7)
summary(sp)

Summary of difference in onsets

Description

Summary of difference in onsets

Usage

summary_onsets(
  onset_obj,
  recording,
  instruments,
  splicing_dfr = NULL,
  expr = NULL,
  show_plot = FALSE,
  filter_pair = NULL,
  na_omit = TRUE,
  time_breaks = NULL
)

Arguments

Value

a summary data frame of onset difference statistics.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), visualise_sample_splices()

Examples

r1 <- get_sample_recording()
o1 <- get_onsets_selected_data(r1)
d1 <- get_duration_annotation_data(r1)
splice_dfr <- splice_time(d1, tier = 'FORM')
summary_onsets(o1, r1, instruments = c('Inst', 'Tabla'),
  splicing_dfr = splice_dfr, show_plot = TRUE)

Velocity plot of a view object

Description

Velocity plot of a view object

Usage

velocity_dp(obj, add_mean = TRUE, vscale = 5, maxpts = 10000, alpha = 0.5, ...)

Arguments

Value

a ggplot object.

Examples

r1 <- get_sample_recording()
rv1 <- get_raw_view(r1, "Central", "", "Sitar")
pv1 <- get_processed_view(rv1)
dp <- c("LWrist", "RWrist", "LElbow", "RElbow", "LEye", "REye", "Neck", "MidHip")
fv1 <- apply_filter_sgolay(pv1, data_point = dp, n = 41, p = 4)
sub_fv1 <- subset(fv1, Time >= 10 & Time <= 20, by = 2)
velocity_dp(sub_fv1)

Visualise random splices

Description

Visualise random splices

Usage

visualise_sample_splices(
  splicing_df,
  splicing_list,
  jv,
  overlay = TRUE,
  avoid_splice_list = list(),
  unstack = FALSE
)

Arguments

Value

a ggplot object.

See Also

Other statistical and analysis functions: apply_column_spliceview(), apply_segment_spliceview(), ave_cross_power_over_splices(), ave_cross_power_spliceview(), ave_power_over_splices(), ave_power_spliceview(), calculate_ave_cross_power1(), calculate_ave_power1(), compare_ave_cross_power1(), compare_ave_power1(), compare_avg_cross_power2(), compare_avg_power2(), difference_onsets(), pull_segment_spliceview(), sample_gap_splice(), sample_offset_splice(), summary_onsets()

Examples

r <- get_sample_recording()
fv_list <- get_filtered_views(r, data_points = 'Nose', n = 41, p = 3)
jv <- get_joined_view(fv_list)
splicing_df <- splice_time(list(a = c(0, 5), b = c(10, 15)))
splicing_list <- sample_offset_splice(splicing_df, jv, num_splices = 20)
visualise_sample_splices(splicing_df, splicing_list, jv)

Get a ggplot2 xlim object based on duration data

Description

Get a ggplot2 xlim object based on duration data

Usage

xlim_duration(object, expr = .data$Tier == "Form")

Arguments

Value

a 'Duration' object.

Examples

r<-get_recording("NIR_ABh_Puriya", fps=25)
m <- get_metre_data(r)
d <- get_duration_annotation_data(r)
autoplot(m)
autoplot(m) + autolayer(d)
v <- get_raw_view(r, "Central", "", "Sitar")
autoplot(v, columns = c("LEar_x", "LEar_y")) + autolayer(d)
autoplot(v, columns = c("LEar_x", "LEar_y")) +
xlim_duration(d, expr = Tier == "FORM" & substr(Comments, 1, 1) == "J") +
autolayer(d, expr = Tier == "FORM" & substr(Comments, 1, 1) == "J")