Return all DVID body annotations

Usage

mcns_dvid_annotations(
  ids = NULL,
  node = "neutu",
  rval = c("data.frame", "list"),
  columns_show = NULL,
  cache = FALSE,
  ...
)

Arguments

ids: A set of body ids in any form understandable to manc_ids
node: A DVID node as returned by manc_dvid_node. The default is to return the current active (unlocked) node being used through neutu.
rval: Whether to return a fully parsed data.frame (the default) or an R list. The data.frame is easier to work with but typically includes NAs for many values that would be missing in the list.
columns_show: Whether to show all columns, or just with '_user', or '_time' suffix. Accepted options are: 'user', 'time', 'all'.
cache: Whether to cache the result of this call for 5 minutes.
...: Additional arguments passed to pbapply::pblapply

Value

A tibble containing with columns including

bodyid as a numeric value
status
user
naming_user
instance
status_user
comment

NB only one bodyid is used regardless of whether the key-value returned has 0, 1 or 2 bodyid fields. When the ids are specified, missing ids will have a row containing the bodyid in question and then all other columns will be NA.

Details

See this Slack post from Stuart Berg for details.

Note that the original api call was <rootuuid>:master, but I have now just changed this to <neutu-uuid> as returned by manc_dvid_node. This was because the range query stopped working 16 May 2021, probably because of a bad node.

Examples

# \donttest{
mda=mcns_dvid_annotations()
head(mda)
#> # A tibble: 6 × 41
#>   bodyid birthtime celltype_predicted_nt celltype_predicted_nt_confidence
#>    <dbl> <chr>     <chr>                                            <dbl>
#> 1  10001 early     acetylcholine                                    0.528
#> 2  10002 NA        acetylcholine                                    0.956
#> 3  10003 early     gaba                                             0.875
#> 4  10005 early     gaba                                             0.831
#> 5  10006 NA        acetylcholine                                    0.820
#> 6  10009 NA        gaba                                             0.866
#> # ℹ 37 more variables: celltype_total_nt_predictions <int>, consensus_nt <chr>,
#> #   flywire_type <chr>, group <int>, hemibrain_type <chr>, instance <chr>,
#> #   itolee_hl <chr>, manc_bodyid <dbl>, manc_group <int>, manc_type <chr>,
#> #   predicted_nt <chr>, predicted_nt_confidence <dbl>, soma_side <chr>,
#> #   status <chr>, subclass <chr>, superclass <chr>, synonyms <chr>,
#> #   total_nt_predictions <int>, type <chr>, supertype <chr>, class <chr>,
#> #   fru_dsx <chr>, dimorphism <chr>, soma_neuromere <chr>, truman_hl <chr>, …
plot(table(mda$type), ylab='Frequency')


kcs=mcns_dvid_annotations("/KC.*")
mbons=mcns_dvid_annotations("/MBON.+")

head(mbons)
#> # A tibble: 6 × 41
#>   bodyid birthtime celltype_predicted_nt celltype_predicted_nt_confidence
#>    <dbl> <chr>     <chr>                                            <dbl>
#> 1 520151 early     glutamate                                        0.714
#> 2  10013 early     glutamate                                        0.714
#> 3 522444 early     glutamate                                        0.742
#> 4 522749 early     glutamate                                        0.742
#> 5 519373 early     glutamate                                        0.768
#> 6 521526 early     glutamate                                        0.768
#> # ℹ 37 more variables: celltype_total_nt_predictions <int>, consensus_nt <chr>,
#> #   flywire_type <chr>, group <int>, hemibrain_type <chr>, instance <chr>,
#> #   itolee_hl <chr>, manc_bodyid <dbl>, manc_group <int>, manc_type <chr>,
#> #   predicted_nt <chr>, predicted_nt_confidence <dbl>, soma_side <chr>,
#> #   status <chr>, subclass <chr>, superclass <chr>, synonyms <chr>,
#> #   total_nt_predictions <int>, type <chr>, supertype <chr>, class <chr>,
#> #   fru_dsx <chr>, dimorphism <chr>, soma_neuromere <chr>, truman_hl <chr>, …
# }