ncmeta/0000755000176200001440000000000014520367022011522 5ustar liggesusersncmeta/NAMESPACE0000644000176200001440000000342614520356511012747 0ustar liggesusers# Generated by roxygen2: do not edit by hand S3method(nc_att,NetCDF) S3method(nc_att,character) S3method(nc_atts,NetCDF) S3method(nc_atts,character) S3method(nc_axes,NetCDF) S3method(nc_axes,character) S3method(nc_axis,NetCDF) S3method(nc_axis,character) S3method(nc_coord_var,NetCDF) S3method(nc_coord_var,character) S3method(nc_dim,NetCDF) S3method(nc_dim,character) S3method(nc_dim,ncdf4) S3method(nc_dims,NetCDF) S3method(nc_dims,character) S3method(nc_dims,ncdf4) S3method(nc_gm_to_prj,data.frame) S3method(nc_gm_to_prj,list) S3method(nc_grid_mapping_atts,NetCDF) S3method(nc_grid_mapping_atts,character) S3method(nc_grid_mapping_atts,data.frame) S3method(nc_grids,NetCDF) S3method(nc_grids,character) S3method(nc_grids,tidync) S3method(nc_inq,NetCDF) S3method(nc_inq,character) S3method(nc_meta,NetCDF) S3method(nc_meta,character) S3method(nc_sources,character) S3method(nc_var,NetCDF) S3method(nc_var,character) S3method(nc_vars,NetCDF) S3method(nc_vars,character) export(nc_att) export(nc_atts) export(nc_axes) export(nc_axis) export(nc_coord_var) export(nc_dim) export(nc_dims) export(nc_gm_to_prj) export(nc_grid_mapping_atts) export(nc_grids) export(nc_inq) export(nc_meta) export(nc_prj_to_gridmapping) export(nc_sources) export(nc_var) export(nc_vars) importFrom(RNetCDF,file.inq.nc) importFrom(dplyr,"%>%") importFrom(dplyr,arrange) importFrom(dplyr,bind_rows) importFrom(dplyr,desc) importFrom(dplyr,distinct) importFrom(dplyr,filter) importFrom(dplyr,group_by) importFrom(dplyr,left_join) importFrom(dplyr,mutate) importFrom(dplyr,row_number) importFrom(dplyr,select) importFrom(dplyr,transmute) importFrom(dplyr,ungroup) importFrom(rlang,.data) importFrom(stats,setNames) importFrom(tibble,as_tibble) importFrom(tibble,tibble) ncmeta/README.md0000644000176200001440000000635414520356511013012 0ustar liggesusers [![R-CMD-check](https://github.com/hypertidy/ncmeta/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/hypertidy/ncmeta/actions/workflows/R-CMD-check.yaml) [![CRAN status](http://www.r-pkg.org/badges/version/ncmeta)](https://cran.r-project.org/package=ncmeta) [![](https://cranlogs.r-pkg.org/badges/ncmeta)](https://cran.r-project.org/package=ncmeta) # ncmeta The `ncmeta` package provides straightforward NetCDF metadata, with a set of consistent entity-based functions for extracting metadata from a file or online source. We aim to fill a gap in between the generality and power of the NetCDF framework and ease of use. There are two main packages for using NetCDF in R, `RNetCDF` and `ncdf4` and ncmeta uses both, where appropriate. Both packages are very close the native API of NetCDF itself, and ncmeta simply provides an easier high-level interpretation. ## About NetCDF NetCDF is both a data model and an API, and provides a very general framework for expressing data formats. The explicit entities in NetCDF are **variables**, **dimensions** and **attributes** and ncmeta provides functions `nc_vars`, `nc_dims`, and `nc_atts` to extract their names, order and other metadata. There are matching functions `nc_var`, `nc_dim`, and `nc_att` with an extra identifier to extract specific information about an individual variable, dimension, or attribute. Also includes functions for implicit entities, these are **grids** and **axes**. These don’t exist in the NetCDF specification explicitly, but are meaningful and worth making explicit. Many NetCDF tools don’t explicitly present these concepts so grab hold of them with ncmeta! A **grid** is an ordered set of dimensions, and the [Unidata site](https://www.unidata.ucar.edu/software/netcdf/) refers *informally* to this concept as **shape**. An **axis** is an *instance* of a dimension, the use of that dimension within a particular variable. These functions provide a more developer-friendly scheme for working with the range of formats provided by the NetCDF ecosystem. ## Installation Install ncmeta from CRAN with: ``` r install.packages("ncmeta") ``` You can install the development version of ncmeta from github with: ``` r # install.packages("devtools") devtools::install_github("hypertidy/ncmeta") ``` ## Example This example shows some of the functions for extracting information from a NetCDF source. ``` r library(ncmeta) filename <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") nc_inq(filename) # one-row summary of file nc_dim(filename, 0) ## first dimension nc_dims(filename) ## all dimensions ``` ## Get involved! Please let us know if you have any feedback, see the [Issues tab](https://github.com/hypertidy/ncmeta) if you found a bug or have a question. Feel free to email the maintainer directly for other questions. ------------------------------------------------------------------------ Please note that this project is released with a [Contributor Code of Conduct](https://github.com/hypertidy/ncmeta/blob/master/CODE_OF_CONDUCT.md#contributor-code-of-conduct). By participating in this project you agree to abide by its terms. ncmeta/man/0000755000176200001440000000000014520356651012303 5ustar liggesusersncmeta/man/nc_dims.Rd0000644000176200001440000000100414520356511014174 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_dimension.R \name{nc_dims} \alias{nc_dims} \alias{nc_dims.character} \alias{nc_dims.NetCDF} \alias{nc_dims.ncdf4} \title{NetCDF dimension} \usage{ nc_dims(x, ...) \method{nc_dims}{character}(x, ...) \method{nc_dims}{NetCDF}(x, ...) \method{nc_dims}{ncdf4}(x, ...) } \arguments{ \item{x}{file address or handle} \item{...}{ignored} } \description{ Get information about the dimensions in a NetCDF source. } ncmeta/man/nc_coord_var.Rd0000644000176200001440000000317114520356511015225 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_coord.R \name{nc_coord_var} \alias{nc_coord_var} \alias{nc_coord_var.character} \alias{nc_coord_var.NetCDF} \title{Get Coordinate Variables for Given Variable} \usage{ nc_coord_var(x, variable = NULL, ...) \method{nc_coord_var}{character}(x, variable = NULL, ...) \method{nc_coord_var}{NetCDF}(x, variable = NULL, ...) } \arguments{ \item{x}{NetCDF source} \item{variable}{variable name of interest. If not included, all variables will be returned.} \item{...}{ignored} } \value{ tibble with "variable", "X", "Y", "Z", "T", and "bounds" columns that reference variables by name. } \description{ In NetCDF, variables are defined along dimensions and are said to have "coordinate variables" that define the (typically spatio-temporal) positions of the data's cells. } \details{ This function attempts to identify the X, Y, Z, and T coordinate variables for each data variable in the provided NetCDF source. The NetCDF-CF attribute conventions are used to make this determination. All variables that can be related to a spatio-temporal axis, including coordinate variables are returned. For coordinate variables, a "bounds" column is included in the response indicating which variable contains bounds information. See \url{http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#coordinate-system} for more. } \examples{ f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") nc_coord_var(f, "chlor_a") f <- system.file("extdata", "guam.nc", package = "ncmeta") nc_coord_var(f) } ncmeta/man/nc_inq.Rd0000644000176200001440000000144314520356511014036 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_inq_file.R \name{nc_inq} \alias{nc_inq} \alias{nc_inq.NetCDF} \alias{nc_inq.character} \title{File info} \usage{ nc_inq(x, ...) \method{nc_inq}{NetCDF}(x, ...) \method{nc_inq}{character}(x, ...) } \arguments{ \item{x}{filename or handle} \item{...}{ignored} } \description{ Get information about a NetCDF data source, may be a file path, or a \code{RNetCDF} file handle, or an OpenDAP/Thredds server address. } \examples{ \donttest{ \dontrun{ f <- raadfiles:::cmip5_files()$fullname[1] nc_inq(f) nc_var(f, 0) nc_dim(f, 0) } } f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") nc_inq(f) nc_var(f, 0) nc_dim(f, 0) nc_vars(f) nc_dims(f) } ncmeta/man/nc_att.Rd0000644000176200001440000000206714520356511014042 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_att.R \name{nc_att} \alias{nc_att} \alias{nc_att.NetCDF} \alias{nc_att.character} \title{NetCDF attributes} \usage{ nc_att(x, variable, attribute, ...) \method{nc_att}{NetCDF}(x, variable, attribute, ...) \method{nc_att}{character}(x, variable, attribute, ...) } \arguments{ \item{x}{or file handle} \item{variable}{name or index (zero based) of variable} \item{attribute}{name or index (zero based) of attribute} \item{...}{ignored} } \value{ data frame of attribute with numeric id, character attribute name, character or numeric variable id or name depending on input, and attribute value. } \description{ Variable attributes are number 0:(n-1). Global attributes are indexed by -1 or the label "NC_GLOBAL". } \details{ \code{nc_inq} includes the number of global attributes \code{nc_vars} includes the number of variable attributes } \examples{ f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") nc_att(f, 0, 0) } ncmeta/man/nc_axis.Rd0000644000176200001440000000110214520356511014203 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_axis.R \name{nc_axis} \alias{nc_axis} \alias{nc_axis.character} \alias{nc_axis.NetCDF} \title{NetCDF axes} \usage{ nc_axis(x, i) \method{nc_axis}{character}(x, i) \method{nc_axis}{NetCDF}(x, i) } \arguments{ \item{x}{NetCDF source} \item{i}{index of axis (1-based, 0 is "empty")} } \description{ An \code{axis} is an instance of a dimension. } \details{ Each data source has a set of dimensions available for use by variables. Each axis is a 1-dimensional instance. } ncmeta/man/nc_grid_mapping_atts.Rd0000644000176200001440000000213314520356511016737 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc-gridmapping.R \name{nc_grid_mapping_atts} \alias{nc_grid_mapping_atts} \alias{nc_grid_mapping_atts.character} \alias{nc_grid_mapping_atts.NetCDF} \alias{nc_grid_mapping_atts.data.frame} \title{Get Grid Mapping} \usage{ nc_grid_mapping_atts(x, data_variable = NULL) \method{nc_grid_mapping_atts}{character}(x, data_variable = NULL) \method{nc_grid_mapping_atts}{NetCDF}(x, data_variable = NULL) \method{nc_grid_mapping_atts}{data.frame}(x, data_variable = NULL) } \arguments{ \item{x}{open NetCDF object, character file path or url to be opened with RNetCDF::open.nc, or data.frame as returned from ncmeta::nc_atts} \item{data_variable}{character variable of interest} } \value{ tibble containing attributes that make up the file's grid_mapping. A data_variable column is included to indicate which data variable the grid mapping belongs to. } \description{ Get the grid mapping from a NetCDF file } \examples{ nc_grid_mapping_atts(system.file("extdata/daymet_sample.nc", package = "ncmeta")) } ncmeta/man/ncmeta-package.Rd0000644000176200001440000000564514520356651015444 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/ncmeta-package.r \docType{package} \name{ncmeta-package} \alias{ncmeta} \alias{ncmeta-package} \title{ncmeta: Straightforward 'NetCDF' Metadata} \description{ Extract metadata from 'NetCDF' data sources, these can be files, file handles or servers. This package leverages and extends the lower level functions of the 'RNetCDF' package providing a consistent set of functions that all return data frames. We introduce named concepts of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 'NetCDF' library \url{https://www.unidata.ucar.edu/software/netcdf/}. 'RNetCDF' matches the library itself with only the named concepts of 'variables', 'dimensions' and 'attributes'. \code{ncmeta} provides a consistent set of tools to obtain metadata from NetCDF. NetCDF is 'Network Common Data Form' https://www.unidata.ucar.edu/software/netcdf/. These functions are generics, allowing methods to be written for various providers so that everything can work from a common basis. All functions return a data frame. } \details{ Each function responds to a character file name or data source (i.e. URL) or to a connection of a given class, this is so a source connection may be created a minimal number of times and kept open while a number of entities are queried. Each "given" entity may be referred to by index (0-based) or name, just as it would be by the NetCDF API and by the two R wrapper providers \code{RNetCDF} and \code{ncdf4}. \tabular{ll}{ \code{\link{nc_att}} \tab find the given attribute of a given variable \cr \code{\link{nc_atts}} \tab find all attributes, of all variables and globals \cr \code{\link{nc_axes}} \tab find all the instances of dimensions \cr \code{\link{nc_axis}} \tab find given instance of a dimension (1-based) \cr \code{\link{nc_dim}} \tab find the given dimension of a source (0-based) \cr \code{\link{nc_dims}} \tab find all the dimensions of a source \cr \code{\link{nc_grids}} \tab find the grids (sets of dimensions) of a source \cr \code{\link{nc_inq}} \tab inquire about a source (i.e. number of dimensions, number of variables, number of global attributes and presence of unlimited dimension \cr \code{\link{nc_meta}} \tab find all metadata for a source (runs all other functions) \cr \code{\link{nc_sources}} \tab tags a record of a source and its "access time" \cr \code{\link{nc_var}} \tab find a given variable (0-based) \cr \code{\link{nc_vars}} \tab find the variables of a source \cr } } \seealso{ Useful links: \itemize{ \item \url{https://github.com/hypertidy/ncmeta} \item Report bugs at \url{https://github.com/hypertidy/ncmeta/issues} } } \author{ \strong{Maintainer}: Michael Sumner \email{mdsumner@gmail.com} Other contributors: \itemize{ \item Tomas Remenyi [contributor] \item Ben Raymond [contributor] \item David Blodgett [contributor] \item Milton Woods [contributor] } } \keyword{internal} ncmeta/man/nc_gm_to_prj.Rd0000644000176200001440000000240214520356511015223 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc-prj.R \name{nc_gm_to_prj} \alias{nc_gm_to_prj} \alias{nc_gm_to_prj.data.frame} \alias{nc_gm_to_prj.list} \title{Get projection from NetCDF-CF Grid Mapping} \usage{ nc_gm_to_prj(x) \method{nc_gm_to_prj}{data.frame}(x) \method{nc_gm_to_prj}{list}(x) } \arguments{ \item{x}{list or data.frame of attributes of the grid mapping variable as returned by ncdf or ncdf4's get attributes functions or ncmeta's nc_grid_mapping_atts.} } \value{ A proj4 string. } \description{ Takes NetCDF-CF grid mapping attributes and returns a proj4 string. } \details{ The WGS84 datum is used as a default if one os not provided in the grid mapping. If only a semi_major axis is provided, a sperical earth is assumed. } \examples{ crs <- list(grid_mapping_name="latitude_longitude", longitude_of_prime_meridian = 0, semi_major_axis = 6378137, inverse_flattening = 298) nc_gm_to_prj(crs) } \references{ \enumerate{ \item \url{https://en.wikibooks.org/wiki/PROJ.4} \item \url{https://trac.osgeo.org/gdal/wiki/NetCDF_ProjectionTestingStatus} \item \url{http://cfconventions.org/cf-conventions/cf-conventions.html#appendix-grid-mappings} } } ncmeta/man/nc_sources.Rd0000644000176200001440000000062314520356511014731 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_sources.R \name{nc_sources} \alias{nc_sources} \alias{nc_sources.character} \title{NetCDF sources} \usage{ nc_sources(x, ...) \method{nc_sources}{character}(x, ...) } \arguments{ \item{x}{data source string} \item{...}{ignored} } \description{ A record of file, URL, or any data source with NetCDF. } ncmeta/man/nc_var.Rd0000644000176200001440000000115214520356511014034 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_var.R \name{nc_var} \alias{nc_var} \alias{nc_var.character} \alias{nc_var.NetCDF} \title{NetCDF variable} \usage{ nc_var(x, i) \method{nc_var}{character}(x, i) \method{nc_var}{NetCDF}(x, i) } \arguments{ \item{x}{file name or handle} \item{i}{variable index (zero based)} } \value{ data frame of variable information } \description{ Return a data frame about the variable at index \code{i}. } \seealso{ \code{nc_vars} to obtain information about all variables, \code{nc_inq} for an overview of the file } ncmeta/man/nc_dim.Rd0000644000176200001440000000134514520356511014021 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_dim.R \name{nc_dim} \alias{nc_dim} \alias{nc_dim.character} \alias{nc_dim.NetCDF} \alias{nc_dim.ncdf4} \title{NetCDF variables Obtain information about a single dimension by index.} \usage{ nc_dim(x, i, ...) \method{nc_dim}{character}(x, i, ...) \method{nc_dim}{NetCDF}(x, i, ...) \method{nc_dim}{ncdf4}(x, i, ...) } \arguments{ \item{x}{filename or handle} \item{i}{index of dimension (zero based)} \item{...}{ignored} } \description{ NetCDF variables Obtain information about a single dimension by index. } \seealso{ \code{nc_vars} to obtain information about all dimensions, \code{nc_inq} for an overview of the file } ncmeta/man/nc_prj_to_gridmapping.Rd0000644000176200001440000000224314520356511017124 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc-gridmapping.R \name{nc_prj_to_gridmapping} \alias{nc_prj_to_gridmapping} \title{Get NetCDF-CF grid mapping from projection} \usage{ nc_prj_to_gridmapping(prj) } \arguments{ \item{prj}{character PROJ string as used in raster, sf, sp, proj4, and rgdal packages.} } \value{ A named list containing attributes required for that grid_mapping. } \description{ Takes a proj4 string and returns a NetCDF-CF projection as a named list of attributes. } \examples{ prj <- "+proj=longlat +datum=NAD27 +no_defs" nc_prj_to_gridmapping(prj) p1 <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96" p2 <- "+x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs" prj2 <- sprintf("\%s \%s", p1, p2) nc_prj_to_gridmapping(prj2) nc_prj_to_gridmapping("+proj=longlat +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs") } \references{ \enumerate{ \item \url{https://en.wikibooks.org/wiki/PROJ.4} \item \url{https://trac.osgeo.org/gdal/wiki/NetCDF_ProjectionTestingStatus} \item \url{http://cfconventions.org/cf-conventions/cf-conventions.html#appendix-grid-mappings} } } ncmeta/man/nc_vars.Rd0000644000176200001440000000075114520356511014223 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_variable.R \name{nc_vars} \alias{nc_vars} \alias{nc_vars.character} \alias{nc_vars.NetCDF} \title{NetCDF variables} \usage{ nc_vars(x, ...) \method{nc_vars}{character}(x, ...) \method{nc_vars}{NetCDF}(x, ...) } \arguments{ \item{x}{filename or handle} \item{...}{ignored currently} } \value{ data frame of variable information } \description{ Generate a table of all variables. } ncmeta/man/nc_atts.Rd0000644000176200001440000000157614520356511014231 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_att.R \name{nc_atts} \alias{nc_atts} \alias{nc_atts.NetCDF} \alias{nc_atts.character} \title{NetCDF attributes} \usage{ nc_atts(x, variable = NULL, ...) \method{nc_atts}{NetCDF}(x, variable = NULL, ...) \method{nc_atts}{character}(x, variable = NULL, ...) } \arguments{ \item{x}{filename or handle} \item{variable}{optional single name of a variable, or 'NC_GLOBAL'} \item{...}{ignored} } \value{ data frame of attributes } \description{ All attributes in the file, globals are treated as if they belong to variable 'NC_GLOBAL'. Attributes for a single variable may be returned by specifying 'variable' - 'NC_GLOBAL' can stand in to return only those attributes. } \examples{ f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") nc_atts(f) } ncmeta/man/nc_axes.Rd0000644000176200001440000000121614520356511014205 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_axes.R \name{nc_axes} \alias{nc_axes} \alias{nc_axes.character} \alias{nc_axes.NetCDF} \title{NetCDF axes} \usage{ nc_axes(x, variables = NULL, ...) \method{nc_axes}{character}(x, variables = NULL, ...) \method{nc_axes}{NetCDF}(x, variables = NULL, ...) } \arguments{ \item{x}{NetCDF source} \item{variables}{names of vars to query} \item{...}{ignored} } \description{ An \code{axis} is an instance of a dimension. } \details{ Each data source has a set of dimensions available for use by variables. Each axis is a 1-dimensional instance. } ncmeta/man/nc_grids.Rd0000644000176200001440000000221014520356511014350 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_grid.R, R/tidync.R \name{nc_grids} \alias{nc_grids} \alias{nc_grids.character} \alias{nc_grids.NetCDF} \alias{nc_grids.tidync} \title{NetCDF grids} \usage{ nc_grids(x, ...) \method{nc_grids}{character}(x, ...) \method{nc_grids}{NetCDF}(x, ...) \method{nc_grids}{tidync}(x, ...) } \arguments{ \item{x}{NetCDF source} \item{...}{ignored} } \description{ A \code{grid} is a discretized space, defined by a set of dimensions. These are the spaces used by one or more variables in a source. Traditional summaries are organized by variable, but when organized by space or grid we can treat multiple variables together using standard database techniques. } \details{ Each data source has a set of dimensions available for use by variables. Each grid is an n-dimensional space available for use by 0, 1 or more variables. A grid only really exists if variable is defined for it, and 'grid' is an implicit entity not an explicit part of the NetCDF API definition. The Unidata pages refer to "shape", which is more or less what we mean by "grid". } ncmeta/man/nc_meta.Rd0000644000176200001440000000202714520356511014174 0ustar liggesusers% Generated by roxygen2: do not edit by hand % Please edit documentation in R/nc_meta.R \name{nc_meta} \alias{nc_meta} \alias{nc_meta.NetCDF} \alias{nc_meta.character} \title{Top level NetCDF metadata.} \usage{ nc_meta(x, ...) \method{nc_meta}{NetCDF}(x, ...) \method{nc_meta}{character}(x, ...) } \arguments{ \item{x}{data source address, file name or handle} \item{...}{ignored} } \description{ This function exists to maintain the open connection while all dimension, variable, and attribute metadata is extracted. } \details{ This function is pretty ambitious, and will send nearly any string to the underlying NetCDF library other than "", which immediately generates an error. This should be robust, but might present fairly obscure error messages from the underlying library. } \examples{ f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") nc_meta(f) \donttest{ \dontrun{ u <- "https://upwell.pfeg.noaa.gov/erddap/tabledap/FRDCPSTrawlLHHaulCatch" nc_meta(u) }} } ncmeta/DESCRIPTION0000644000176200001440000000275714520367022013243 0ustar liggesusersPackage: ncmeta Title: Straightforward 'NetCDF' Metadata Version: 0.3.6 Authors@R: c(person("Michael", "Sumner", email = "mdsumner@gmail.com", role = c("aut", "cre")), person("Tomas", "Remenyi", role = "ctb"), person("Ben", "Raymond", role = "ctb"), person("David", "Blodgett", role = "ctb"), person("Milton", "Woods", role = "ctb")) Description: Extract metadata from 'NetCDF' data sources, these can be files, file handles or servers. This package leverages and extends the lower level functions of the 'RNetCDF' package providing a consistent set of functions that all return data frames. We introduce named concepts of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 'NetCDF' library . 'RNetCDF' matches the library itself with only the named concepts of 'variables', 'dimensions' and 'attributes'. Depends: R (>= 3.3.0) License: GPL-3 Encoding: UTF-8 RoxygenNote: 7.2.3 Imports: dplyr, rlang, RNetCDF, tibble, stats, tidyr Suggests: testthat, covr URL: https://github.com/hypertidy/ncmeta BugReports: https://github.com/hypertidy/ncmeta/issues ByteCompile: TRUE NeedsCompilation: no Packaged: 2023-11-01 05:46:46 UTC; gdal Author: Michael Sumner [aut, cre], Tomas Remenyi [ctb], Ben Raymond [ctb], David Blodgett [ctb], Milton Woods [ctb] Maintainer: Michael Sumner Repository: CRAN Date/Publication: 2023-11-01 06:20:02 UTC ncmeta/tests/0000755000176200001440000000000014520356511012665 5ustar liggesusersncmeta/tests/testthat/0000755000176200001440000000000014520367022014524 5ustar liggesusersncmeta/tests/testthat/test-attributes.R0000644000176200001440000000715514520356511020023 0ustar liggesuserscontext("attributes") f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") u <- "https://upwell.pfeg.noaa.gov/erddap/tabledap/FRDCPSTrawlLHHaulCatch" test_that("attributes works", { testthat::skip_on_cran() met <- nc_meta(f) da <- nc_atts(f) %>% expect_s3_class("tbl_df") %>% expect_named(c("id", "name", "variable", "value")) expect_that(nrow(da), equals(87L)) expect_that(da$value, is_a("list")) da <- nc_atts(f, add_names = TRUE) %>% expect_s3_class("tbl_df") %>% expect_named(c("id", "name", "variable", "value")) }) test_that("attributes from Thredds works", { context("avoiding thredds tests for RNetCDF") skip_on_cran() ## skip() ## github can't do this atm 2022-08-15 du <- try(nc_atts(u)) if (!inherits(du, "try-error")) { du %>% expect_s3_class("tbl_df") %>% expect_named(c("id", "name", "variable", "value")) # expect_that(nrow(du), equals(119L)) ## became 124 rows in 2022 August expect_that(du$value, is_a("list")) } }) test_that("individual attribute inquiry works", { testthat::skip_on_cran() nc_att(f, 0, 0) %>% expect_s3_class("tbl_df") %>% expect_named(c("id", "name", "variable", "value")) a3 <- nc_att(f, 0, 3) expect_that(a3$id, equals(3.0)) expect_that(a3$name, equals("_FillValue")) chk <- list(`_FillValue` = -32767) expect_that(a3$value, equals(chk)) expect_identical(a3, nc_att(f, 0, "_FillValue")) }) l3binfile <- system.file("extdata", "S2008001.L3b_DAY_CHL.nc", package = "ncmeta") test_that("failure is graceful", { expect_warning(abin <- nc_atts(l3binfile), "no variables recognizable") abin %>% expect_s3_class("tbl_df") %>% expect_named(c("id", "name", "variable", "value")) expect_that(nrow(abin), equals(49L)) expect_that(abin$variable, equals(rep("NC_GLOBAL",49L))) expect_that(abin$name, equals(c("product_name", "title", "instrument", "platform", "temporal_range", "start_orbit_number", "end_orbit_number", "date_created", "processing_version", "history", "time_coverage_start", "time_coverage_end", "northernmost_latitude", "southernmost_latitude", "easternmost_longitude", "westernmost_longitude", "geospatial_lat_max", "geospatial_lat_min", "geospatial_lon_max", "geospatial_lon_min", "geospatial_lat_units", "geospatial_lon_units", "geospatial_lon_resolution", "geospatial_lat_resolution", "spatialResolution", "data_bins", "percent_data_bins", "units", "binning_scheme", "project", "institution", "standard_name_vocabulary", "Metadata_Conventions", "Conventions", "naming_authority", "id", "license", "creator_name", "publisher_name", "creator_email", "publisher_email", "creator_url", "publisher_url", "processing_level", "cdm_data_type", "identifier_product_doi_authority", "identifier_product_doi", "keywords", "keywords_vocabulary"))) expect_that(unique(abin$id), equals(-1)) }) test_that("nc_atts works", { ## https://github.com/hypertidy/ncmeta/issues/36 f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") expect_equal(nc_atts(f, "NC_GLOBAL")$variable, rep("NC_GLOBAL", 65)) expect_equal(nrow(nc_atts(f)), 87) expect_equal(nrow(nc_atts(f, "chlor_a")), 12) expect_equal(nrow(nc_atts(f, "lon")), 5) }) ncmeta/tests/testthat/test-gridmapping-prj.R0000644000176200001440000004113414520356511020722 0ustar liggesuserscontext("prj and grid mappings") test_that("nc_grid_mapping_atts", { nc <- system.file("extdata/S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") expect_warning(gm <- nc_grid_mapping_atts(nc), paste("No variables with a grid mapping found.\n", "Defaulting to WGS84 Lon/Lat")) expect_equal(stats::setNames(gm$value, gm$name), list(grid_mapping_name = "latitude_longitude", semi_major_axis = 6378137, inverse_flattening = 298.257223563, longitude_of_prime_meridian = 0)) nc <- system.file("extdata/daymet_sample.nc", package = "ncmeta") gm <- nc_grid_mapping_atts(nc) expect_true(all(list(grid_mapping_name = "lambert_conformal_conic", longitude_of_central_meridian = -100, latitude_of_projection_origin = 42.5, false_easting = 0, false_northing = 0, standard_parallel = c(25, 60), semi_major_axis = 6378137, inverse_flattening = 298.257223563, longitude_of_prime_meridian = 0) %in% stats::setNames(gm$value, gm$name))) expect_is(nc_grid_mapping_atts(ncmeta::nc_atts(nc)), "data.frame") expect_is(nc_grid_mapping_atts(RNetCDF::open.nc(nc)), "data.frame") gm2 <- nc_grid_mapping_atts(nc, data_variable = "prcp") expect_equal(nrow(gm), nrow(gm2)) expect_warning(nc_grid_mapping_atts(nc, data_variable = "borked"), "no grid_mapping attribute found for this variable") }) test_that("nc_prj_to_gridmapping returns an empty list if no mapping exists", { p <- "" expect_warning(crs <- nc_prj_to_gridmapping(p), "not a valid crs, returning an empty tibble") expect_equal(names(crs), c("name", "value")) expect_equal(nrow(crs), 0) }) test_that("wgs 84 lat lon", { p <- "+proj=longlat +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name="latitude_longitude", longitude_of_prime_meridian = 0, semi_major_axis = 6378137, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs, c[names(crs)]) }) test_that("NAD27 lat lon", { p <- "+proj=longlat +datum=NAD27 +no_defs" p2 <- "+proj=longlat +a=6378206.4 +f=0.00339007530392762 +pm=0 +no_defs" c <- list(grid_mapping_name="latitude_longitude", longitude_of_prime_meridian = 0, semi_major_axis = 6378206.4, inverse_flattening = 294.978698214) prj <- nc_gm_to_prj(c) expect_equal(prj, p2) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs, c[names(crs)]) }) test_that("albers equal area epsg:5070", { p <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0 +units=m +lat_0=23 +lon_0=-96 +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "albers_conical_equal_area", longitude_of_central_meridian = -96, latitude_of_projection_origin = 23, false_easting = 0.0, false_northing = 0.0, standard_parallel = c(29.5, 45.5), semi_major_axis = 6378137.0, inverse_flattening = 298.257223563, longitude_of_prime_meridian = 0) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs, c[names(crs)]) }) test_that("albers equal area epsg:5070 with datum instead of a b", { p <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 +x_0=0 +y_0=0 +units=m +datum=NAD83 +units=m +no_defs +ellps=GRS80 +towgs84=0,0,0" c <- list(grid_mapping_name = "albers_conical_equal_area", longitude_of_central_meridian = -96, latitude_of_projection_origin = 23, false_easting = 0.0, false_northing = 0.0, standard_parallel = c(29.5, 45.5), semi_major_axis = 6378137.0, inverse_flattening = 298.257222101, longitude_of_prime_meridian = 0) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs, c[names(crs)]) }) test_that("Azimuthal Equidistant", { p <- "+proj=aeqd +lat_0=30 +lon_0=-40 +x_0=0 +y_0=0 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "azimuthal_equidistant", longitude_of_projection_origin = -40, latitude_of_projection_origin = 30, false_easting = 0.0, false_northing = 0.0, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) }) test_that("lambert conformal conic daymet", { p <- "+proj=lcc +lat_1=25 +lat_2=60 +x_0=0 +y_0=0 +units=m +lat_0=42.5 +lon_0=-100 +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "lambert_conformal_conic", longitude_of_central_meridian = -100.0, latitude_of_projection_origin = 42.5, false_easting = 0.0, false_northing = 0.0, standard_parallel = c(25.0, 60.0), longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) }) test_that("lambert_azimuthal_equal_area", { p <- "+proj=laea +lat_0=90 +lon_0=0 +x_0=0 +y_0=0 +units=m +a=6371228 +b=6371228 +pm=0 +no_defs" c <- list(grid_mapping_name = "lambert_azimuthal_equal_area", longitude_of_projection_origin = 0, latitude_of_projection_origin = 90, false_easting = 0.0, false_northing = 0.0, semi_major_axis = 6371228, semi_minor_axis = 6371228, longitude_of_prime_meridian = 0.0) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) c <- list(grid_mapping_name = "lambert_azimuthal_equal_area", longitude_of_projection_origin = 0, latitude_of_projection_origin = 90, false_easting = 0.0, false_northing = 0.0, longitude_of_prime_meridian = 0.0, semi_major_axis = 6371228) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) }) test_that("lambert_cylindrical_equal_area", { p <- "+proj=cea +lon_0=0 +lat_ts=0 +x_0=0 +y_0=0 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "lambert_cylindrical_equal_area", longitude_of_central_meridian = 0, standard_parallel=0, false_easting = 0.0, false_northing = 0.0, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) }) test_that("mercator", { p <- "+proj=merc +lon_0=0 +lat_ts=0 +x_0=0 +y_0=0 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "mercator", longitude_of_projection_origin = 0, standard_parallel=0, false_easting = 0.0, false_northing = 0.0, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) p <- "+proj=merc +lon_0=0 +k=1 +x_0=0 +y_0=0 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "mercator", longitude_of_projection_origin = 0, scale_factor_at_projection_origin=1, false_easting = 0.0, false_northing = 0.0, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) }) test_that("oblique_mercator", { p <- "+proj=omerc +lat_0=45.3091666666667 +lonc=-86 +k=0.9996 +alpha=337.25556 +gamma=337.25556 +no_uoff +x_0=2546731.496 +y_0=-4354009.816 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "oblique_mercator", azimuth_of_central_line = 337.25556, longitude_of_projection_origin = -86, latitude_of_projection_origin = 45.3091666666667, scale_factor_at_projection_origin = 0.9996, false_easting = 2546731.496, false_northing = -4354009.816, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) }) test_that("orthographic", { p <- "+proj=ortho +lat_0=30 +lon_0=-40 +x_0=0 +y_0=0 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "orthographic", longitude_of_projection_origin = -40, latitude_of_projection_origin = 30, false_easting = 0.0, false_northing = 0.0, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) }) test_that("polar_stereographic", { p <- "+proj=stere +lat_0=-90 +lon_0=0 +k=1 +x_0=0 +y_0=0 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "polar_stereographic", straight_vertical_longitude_from_pole = 0, latitude_of_projection_origin = -90, scale_factor_at_projection_origin = 1, false_easting = 0.0, false_northing = 0.0, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) # crs <- nc_prj_to_gridmapping(p) # crs <- stats::setNames(crs$value, crs$name) # # expect_equal(crs[names(c)], c) p <- "+proj=stere +lat_0=-90 +lon_0=0 +lat_ts=-71 +x_0=0 +y_0=0 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "polar_stereographic", straight_vertical_longitude_from_pole = 0, latitude_of_projection_origin = -90, standard_parallel = -71, false_easting = 0.0, false_northing = 0.0, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) # crs <- nc_prj_to_gridmapping(p) # crs <- stats::setNames(crs$value, crs$name) # # expect_equal(crs[names(c)], c) }) test_that("sinusoidal", { p <- "+proj=sinu +lon_0=0 +x_0=0 +y_0=0 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "sinusoidal", longitude_of_projection_origin = 0, false_easting = 0.0, false_northing = 0.0, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) }) test_that("stereographic", { p <- "+proj=stere +lat_0=46.5 +lon_0=-66.5 +k=0.999912 +x_0=2500000 +y_0=7500000 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "stereographic", longitude_of_projection_origin = -66.5, latitude_of_projection_origin = 46.5, scale_factor_at_projection_origin = 0.999912, false_easting = 2500000, false_northing = 7500000, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) }) test_that("transverse_mercator", { p <- "+proj=tmerc +lat_0=53.5 +lon_0=-8 +k=0.99982 +x_0=600000 +y_0=750000 +units=m +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs" c <- list(grid_mapping_name = "transverse_mercator", longitude_of_central_meridian = -8, latitude_of_projection_origin = 53.5, scale_factor_at_central_meridian = 0.99982, false_easting = 600000, false_northing = 750000, longitude_of_prime_meridian = 0.0, semi_major_axis = 6378137.0, inverse_flattening = 298.257223563) prj <- nc_gm_to_prj(c) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs[names(c)], c) }) test_that("spherical", { p <- "+proj=lcc +lat_1=30 +lat_2=60 +x_0=0 +y_0=0 +units=m +lat_0=40.0000076294 +lon_0=-97 +a=6370000 +b=6370000 +pm=0 +no_defs" # From the National Water Model forcings cl <- list(transform_name = "lambert_conformal_conic", grid_mapping_name = "lambert_conformal_conic", standard_parallel = c(30, 60), longitude_of_central_meridian = -97, latitude_of_projection_origin = 40.0000076294, false_easting = 0, false_northing = 0, earth_radius = 6370000) prj <- suppressWarnings(nc_gm_to_prj(cl)) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs, list(grid_mapping_name = "lambert_conformal_conic", standard_parallel = c(30, 60), false_easting = 0, false_northing = 0, latitude_of_projection_origin = 40.0000076294, longitude_of_central_meridian = -97, semi_major_axis = 6370000, semi_minor_axis = 6370000, longitude_of_prime_meridian = 0)) }) test_that("more spherical", { p <- "+proj=lcc +lat_1=30 +lat_2=65 +x_0=-6000 +y_0=-6000 +units=m +lat_0=48 +lon_0=9.75 +a=6371229 +b=6371229 +pm=0 +no_defs" # from "https://github.com/mdsumner//rasterwise/extdata/EURO-CORDEX_81_DOMAIN000_54/EURO-CORDEX_81_DOMAIN000.nc" cl <- list(proj4_params = "+proj=lcc +lat_1=30.00 +lat_2=65.00 +lat_0=48.00 +lon_0=9.75 +x_0=-6000. +y_0=-6000. +ellps=sphere +a=6371229. +b=6371229. +units=m +no_defs", grid_mapping_name = "lambert_conformal_conic", standard_parallel = c(30, 65), longitude_of_central_meridian = 9.75, latitude_of_projection_origin = 48, semi_major_axis = 6371229, inverse_flattening = 0, # This is the BS that caused an Inf! false_easting = -6000, false_northing = -6000, `_CoordinateTransformType` = "Projection", `_CoordinateAxisTypes` = "GeoX GeoY") prj <- suppressWarnings(nc_gm_to_prj(cl)) expect_equal(prj, p) crs <- nc_prj_to_gridmapping(p) crs <- stats::setNames(crs$value, crs$name) expect_equal(crs, list(grid_mapping_name = "lambert_conformal_conic", standard_parallel = c(30, 65), false_easting = -6000, false_northing = -6000, latitude_of_projection_origin = 48, longitude_of_central_meridian = 9.75, semi_major_axis = 6371229, semi_minor_axis = 6371229, longitude_of_prime_meridian = 0)) }) ncmeta/tests/testthat/test-file-bogatron.R0000644000176200001440000000043114520356511020353 0ustar liggesusers# library(testthat) # # context("file-bogatron") # test_that({ # skip_if_not(we_are_raady()) # # sstd1 <- raadtools::sstfiles()$fullname[1] # # sstm1 <- # # raadtools::sstfiles(time.resolution = "monthly")$fullname[1] # # rom1 <- raadtools::cpolarfiles()$fullname[1] # }) ncmeta/tests/testthat/test-vars.R0000644000176200001440000000237314520356511016605 0ustar liggesuserscontext("vars") f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") test_that("vars works", { v <- nc_vars(f) v %>% expect_s3_class("tbl_df") %>% expect_named(c("id", "name", "type", "ndims", "natts")) expect_that(v$id, equals(0:3)) expect_that(v$name, equals(c("chlor_a", "lat", "lon", "palette"))) ## 2018-02-09 ## this test depends on the env, e.g. UNKNOWN on Windows and Mac, UC_BYTE on Ubuntu ## must be RNetCDF version? # expect_that(v$type, equals(c("NC_FLOAT", "NC_FLOAT", "NC_FLOAT", "NC_UBYTE"))) expect_that(v$ndims, equals(c(2, 1, 1, 2))) expect_that(v$natts, equals(c(12, 5, 5, 0))) }) test_that('grids works', { ## FIXME skip("fixme") ## only in nc-grids-normal branch g <- nc_grids(f) s <- nc_sources(f) v <- nc_var(f, 1) g %>% expect_s3_class("tbl_df") %>% expect_named(c("variable", "grid")) s %>% expect_s3_class("tbl_df") %>% expect_named(c("access", "source")) v %>% expect_s3_class("tbl_df") %>% expect_named(c("id", "name", "type", "ndims", "natts")) expect_that(g[["variable"]], equals(c("chlor_a", "palette", "lat", "lon"))) }) test_that("nc4 bug", { f <- system.file("extdata/gridmet_sample.nc", package = "ncmeta") vars <- nc_vars(f) expect_equal(nrow(vars), 5) }) ncmeta/tests/testthat/test-dimension.R0000644000176200001440000000243014520356511017611 0ustar liggesuserscontext("dimension") f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") test_that("file specific dimension inquiry works", { dim0 <- nc_dim(f, 0) %>% expect_s3_class("tbl_df") expect_that(nrow(dim0), equals(1L)) expect_that(dim0$id, equals(0)) expect_that(dim0$name, equals("lat")) expect_that(dim0$length, equals(2160)) ## double expect_that(dim0$unlim, equals(FALSE)) # expect_that(unlist(lapply(dim0, typeof)), # equals(structure(c("integer", "character", "double", "logical"), .Names = c("id", # "name", "length", "unlim")))) }) test_that("file all dimensions inquiry works", { dimension <- nc_dims(f) %>% expect_s3_class("tbl_df") expect_that(nrow(dimension), equals(4L)) expect_that(dimension$id, equals(0:3)) expect_that(dimension$name, equals(c("lat", "lon", "rgb", "eightbitcolor"))) expect_that(dimension$length, equals(c(2160, 4320, 3, 256))) ## double expect_false(any(dimension$unlim)) #expect_that(unlist(lapply(dimension, typeof)), # equals(structure(c("integer", "character", "double", "logical"), # .Names = c("id","name", "length", "unlim")))) }) ncmeta/tests/testthat/test-grid-var-order.R0000644000176200001440000000074214520356511020454 0ustar liggesuserscontext("test-grid-var-order") f <- system.file("extdata/stars/reduced.nc", package= "ncmeta") test_that("variable order by grid is correct", { ## FIXME skip("fixme") ## only in nc-grids-normal branch ## descending by ndims, but otherwise native to the source vars_sort_by_ndims <- nc_vars(f) %>% dplyr::arrange(desc(ndims)) %>% dplyr::pull(name) expect_equal(nc_meta(f)$grid$variable, vars_sort_by_ndims) expect_equal(nc_grids(f)$variable, vars_sort_by_ndims) }) ncmeta/tests/testthat/test-coord.R0000644000176200001440000000703014520356511016733 0ustar liggesuserscontext("nc_coord_var") f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") nc <- RNetCDF::open.nc(f) test_that("nc_coord_var brings back expected content for one variable", { expect_equivalent(nc_coord_var(f, "chlor_a"), data.frame(variable = "chlor_a", X = "lon", Y = "lat", Z = NA_character_, T = NA_character_, bounds = NA_character_, stringsAsFactors = FALSE)) expect_equal(nc_coord_var(f, "pallete"), NULL) expect_equal(nc_coord_var(f, "lat")$Y, "lat") }) test_that("nc_coord_vars brings back expected content for sample", { coord_vars <- nc_coord_var(f) expect_equivalent(dplyr::filter(coord_vars, variable == "chlor_a"), data.frame(variable = "chlor_a", X = "lon", Y = "lat", Z = NA_character_, T = NA_character_, bounds = NA_character_, stringsAsFactors = FALSE)) }) f <- system.file("extdata", "guam.nc", package = "ncmeta") nc <- RNetCDF::open.nc(f) test_that("nc_coord_var brings back expected content for one variable", { expect_equivalent(nc_coord_var(f, "RAINNC_present"), data.frame(variable = "RAINNC_present", X = "XLONG", Y = "XLAT", Z = NA_character_, T = "Time", bounds = NA_character_, stringsAsFactors = FALSE)) expect_equal(nc_coord_var(f, "XLAT"), NULL) expect_equal(nc_coord_var(f, "Time")$T, "Time") }) test_that("nc_coord_vars brings back expected content for sample", { coord_vars <- nc_coord_var(f) expect_true(nrow(coord_vars) == 5) coord_vars <- coord_vars[coord_vars$variable != "Time", ] expect_true(nrow(coord_vars) == 4) expect_true(all(coord_vars$X == "XLONG")) expect_true(all(coord_vars$Y == "XLAT")) expect_true(all(is.na(coord_vars$Z))) expect_true(all(coord_vars$T == "Time")) }) test_that("slightly broken projected coordinates work", { f <- system.file("extdata", "daymet_sample.nc", package = "ncmeta") expect_warning(coord_vars <- nc_coord_var(f), "missing coordinate variables names in coordinates attribute trying to find non-auxiliary coordinate variables.") expect_equal(as.character(coord_vars[coord_vars$variable == "prcp", ]), c("prcp", "x", "y", NA, "time", NA)) expect_true(nrow(coord_vars) == 4) expect_equal(as.character(coord_vars[coord_vars$variable == "time", ]), c("time", NA, NA, NA, "time", "time_bnds")) }) test_that("degen z", { f <- system.file("extdata/avhrr-only-v2.19810901_header.nc", package = "ncmeta") coord_vars <- nc_coord_var(f) expect_true(all(is.na(coord_vars$Z))) }) test_that("timeseries", { f <- system.file("extdata/rasterwise-timeseries.nc", package = "ncmeta") coord_vars <- nc_coord_var(f) expect_equal(as.character(coord_vars[coord_vars$variable == "pr",]), c("pr", "lon", "lat", NA, "time", NA)) expect_true(nrow(coord_vars) == 2) }) test_that("high dim", { f <- system.file("extdata/rasterwise-high-dim-test-1.nc", package = "ncmeta") coord_vars <- nc_coord_var(f) expect_true(nrow(coord_vars) == 0) }) test_that("all the things", { f <- system.file("extdata/rasterwise-bad_examples_62-example3.nc", package = "ncmeta") coord_vars <- nc_coord_var(f) expect_true(sum(coord_vars$variable == "pr") == 2) }) ncmeta/tests/testthat/test-tidync.R0000644000176200001440000000227414520356511017124 0ustar liggesuserscontext("test-tidync.R") context("filter") # x1 <- tidync(f1) %>% activate("D5,D12") %>% hyper_filter(QCcheckNum = index < 2) # x2 <- tidync(f1) %>% activate("D5,D12") %>% hyper_filter() # # nc_vars(f1) # ## dimensions without variables # x1 %>% expect_s3_class("tidync") # x2 %>% expect_s3_class("tidync") test_that("file open and metadata is ok", { skip_on_cran() f1 <- system.file("extdata/madishydro.nc", package = "ncmeta") skip_if_not(file.exists(f1)) nc_axes(f1) %>% expect_s3_class("tbl_df") %>% expect_named(c("axis", "variable", "dimension")) }) #test_that("indexing works", { # l3file <- system.file("extdata/oceandata", # "S20080012008031.L3m_MO_CHL_chlor_a_9km.nc", package = "tidync") # ind0 <- tidync(l3file) %>% hyper_filter() # expect_that(ind0$dimension$count[ind0$dimension$active], equals(c(2160L, 4320L))) # ind1 <- tidync(l3file) %>% hyper_filter(lon = index == 100) # expect_that(ind1$dimension$count[ind0$dimension$active], equals(c(2160L, 1L))) # expect_warning(ind2 <- tidync(l3file) %>% hyper_filter(lon = index %% 100 == 0)) # expect_that(ind2$dimension$count[ind2$dimension$active], equals(c(2160, 4201))) #}) ncmeta/tests/testthat/test-file-bogeys.R0000644000176200001440000000206714520356511020037 0ustar liggesuserscontext("file-bogeys") test_that("files and bad files are handled", { skip_if_not(we_are_raady()) oisst_dayfile <- raadfiles::oisst_daily_files()$fullname[1] nc_meta(oisst_dayfile) %>% expect_named(c("dimension", "variable", "attribute", "axis", "grid", "source" )) oisst_monfile <- raadfiles::oisst_monthly_files()$fullname[1] nc_meta(oisst_monfile) %>% expect_named(c("dimension", "variable", "attribute", "axis", "grid", "source" )) roms_file <- raadtools::cpolarfiles()$fullname[1] nc_meta(roms_file) %>% expect_named(c("dimension", "variable", "attribute", "axis", "grid", "source" )) l3_file <- raadtools::ocfiles()$fullname[1] expect_error(nc_vars(l3_file), "NetCDF: HDF error") }) test_that("bad files and URLs fail gracefully", { skip_on_travis() ## why does tis fail so badly on travis? skip_on_cran() expect_error(nc_meta(""), "empty string") expect_error(nc_meta(), "must be a valid NetCDF source, filename or URL") expect_error(nc_meta("https://abc")) ## let's not worry about the actual error: "NetCDF: I/O failure" }) ncmeta/tests/testthat/test-file.R0000644000176200001440000000250714520356511016550 0ustar liggesuserscontext("file") f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") #inq_structure <- structure(c("integer", "integer", "integer", "integer", "character" #), .Names = c("ndims", "nvars", "ngatts", "unlimdimid", "filename" #)) test_that("file inquiry works", { inq <- nc_inq(f) %>% expect_s3_class("tbl_df") expect_that(nrow(inq), equals(1L)) expect_that(inq$ndims, equals(4L)) expect_that(inq$nvars, equals(4L)) expect_that(inq$ngatts, equals( 65L)) expect_true(is.na(inq$unlimdimid)) # expect_that(unlist(lapply(inq, typeof)), # equals(inq_structure)) }) test_that("multiple file inquiry works", { inqfiles <- nc_inq(c(f, f)) %>% expect_s3_class("tbl_df") expect_that(nrow(inqfiles), equals(2L)) expect_that(unique(inqfiles$ndims), equals(4L)) expect_that(unique(inqfiles$nvars), equals(4L)) expect_that(unique(inqfiles$ngatts), equals( 65L)) expect_true(is.na(unique(inqfiles$unlimdimid))) # expect_that(unlist(lapply(inqfiles, typeof)), # equals(inq_structure)) }) # test_that("thredds access works", { # skip_on_cran() # skip() ## ERDDAP hates us now # u <- "https://upwell.pfeg.noaa.gov/erddap/tabledap/FRDCPSTrawlLHHaulCatch" # thredds <- nc_inq(u) %>% expect_s3_class("tbl_df") # expect_that(nrow(thredds), equals(1L)) # # }) ncmeta/tests/testthat/test-utils.R0000644000176200001440000000144714520356511016773 0ustar liggesuserscontext("nc utils") test_that("get_var_by_att", { nc <- system.file("extdata/daymet_sample.nc", package = "ncmeta") expect_true(ncmeta:::find_var_by_att(nc, "coordinates") == "prcp") expect_true(length(ncmeta:::find_var_by_att(nc, "units")) == 4) expect_true(length(ncmeta:::find_var_by_att( nc, "long_name", "coordinate of projection", strict = FALSE)) == 2) expect_true(length(ncmeta:::find_var_by_att( nc, "long_name", "coordinate of projection", strict = TRUE)) == 0) expect_true(length(ncmeta:::find_var_by_att( nc, "units", "mm/day", strict = TRUE)) == 1) nc <- RNetCDF::open.nc(nc) expect_true(ncmeta:::find_var_by_att(nc, "coordinates") == "prcp") expect_true(ncmeta:::find_var_by_att(ncmeta::nc_atts(nc), "coordinates") == "prcp") }) ncmeta/tests/testthat.R0000644000176200001440000000024614520356511014652 0ustar liggesuserssysname <- tolower(Sys.info()[["sysname"]]) if (!"sunos" %in% sysname) { library(testthat) library(ncmeta) #skip_on_os("solaris") test_check("ncmeta") }ncmeta/R/0000755000176200001440000000000014520356511011724 5ustar liggesusersncmeta/R/tidync.R0000644000176200001440000000012514520356511013337 0ustar liggesusers#' @name nc_grids #' @export nc_grids.tidync <- function(x, ...) { x[["grids"]] }ncmeta/R/nc_variable.R0000644000176200001440000000157314520356511014322 0ustar liggesusers #' NetCDF variables #' #' Generate a table of all variables. #' @param x filename or handle #' @param ... ignored currently #' #' @return data frame of variable information #' @export #' nc_vars <- function(x, ...) { UseMethod("nc_vars") } #' @name nc_vars #' @export nc_vars.character <- function(x, ...) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_vars(nc) } #' @name nc_vars #' @export #' @importFrom dplyr %>% #' @importFrom rlang .data nc_vars.NetCDF <- function(x, ...) { nvars <- nc_inq(x)$nvars if (nvars < 1) return(tibble::tibble()) nc_vars_internal(x, nvars) } nc_vars_internal <- function(x, nvars) { dplyr::bind_rows(lapply(seq_len(nvars), function(i) nc_var(x, i-1))) %>% dplyr::distinct(.data$id, .data$name, .data$type, .data$ndims, .data$natts) }ncmeta/R/nc_att.R0000644000176200001440000001050514520356511013320 0ustar liggesusers#' NetCDF attributes #' #' Variable attributes are number 0:(n-1). Global attributes are indexed #' by -1 or the label "NC_GLOBAL". #' #' `nc_inq` includes the number of global attributes #' `nc_vars` includes the number of variable attributes #' @param x or file handle #' @param variable name or index (zero based) of variable #' @param attribute name or index (zero based) of attribute #' @param ... ignored #' #' @return data frame of attribute with numeric id, character attribute name, #' character or numeric variable id or name depending on input, and attribute value. #' @export #' #' @examples #' f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") #' nc_att(f, 0, 0) #' @name nc_att #' @export nc_att <- function(x, variable, attribute, ...) { UseMethod("nc_att") } #' @name nc_att #' @export #' @importFrom rlang .data #' @importFrom stats setNames nc_att.NetCDF <- function(x, variable, attribute, ...) { att_info <- RNetCDF::att.inq.nc(x, variable, attribute) ## att <- structure(RNetCDF::att.get.nc(x, variable, attribute), names = att_info$name) att <- RNetCDF::att.get.nc(x, variable, attribute) tibble::as_tibble(list(id = att_info$id, name = att_info$name, variable = variable, value = setNames(list(att), att_info$name))) } #' @name nc_att #' @export #' @importFrom tibble tibble nc_att.character <- function(x, variable, attribute, ...) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_att(nc, variable, attribute) } #' NetCDF attributes #' #' All attributes in the file, globals are treated as if they belong to variable 'NC_GLOBAL'. Attributes #' for a single variable may be returned by specifying 'variable' - 'NC_GLOBAL' can stand in to return #' only those attributes. #' @param x filename or handle #' @param variable optional single name of a variable, or 'NC_GLOBAL' #' @param ... ignored #' #' @return data frame of attributes #' @export #' #' @examples #' f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") #' nc_atts(f) nc_atts <- function(x, variable = NULL, ...) { UseMethod("nc_atts") } #' @name nc_atts #' @export #' @importFrom dplyr distinct #' @importFrom tibble tibble nc_atts.NetCDF <- function(x, variable = NULL, ...) { global <- tibble::as_tibble(list(id = -1, name = "NC_GLOBAL", natts = nc_inq(x)$ngatts)) #vars <- nc_axes(x) vars <- try(nc_vars(x), silent = TRUE) ## bomb out if ndims is NA if (inherits(vars, "try-error") || nrow(vars) < 1L) { warning("no variables recognizable") atts <- lapply(seq_len(global$natts), function(a) nc_att(x, "NC_GLOBAL", a - 1)) if (length(atts) > 0) { value <- unlist(lapply(atts, function(b) b$value), recursive = FALSE) name <- unlist(lapply(atts, function(b) b$name)) } else { value <- list() name <- character(0) } global <- tibble::tibble(id = -1, name = name, variable = "NC_GLOBAL", value = value) return(global) } else { #var <- dplyr::distinct(vars, .data$id, .data$name, .data$natts) var <- vars[, c("id", "name", "natts")] var <- var[!duplicated(var), ] var <- dplyr::bind_rows(var, global) if (!is.null(variable)) { var <- dplyr::filter(var, .data$name == variable[1]) } } if (!is.null(variable) && !variable %in% var$name) stop("specified variable not found") #bind_rows(lapply(split(var, var$name), function(v) bind_rows(lapply(seq_len(v$natts), function(iatt) nc_att(x, v$name, iatt - 1))))) #bind_rows <- function(x) x if (any(var$natts > 0)) { out <- dplyr::bind_rows(lapply(split(var, var$name)[unique(var$name)], function(v) dplyr::bind_rows(lapply(seq_len(v$natts), function(iatt) nc_att(x, v$name, iatt - 1))))) } else { out <- tibble::tibble(id = double(0), name = character(0), variable = character(0), value = list()) } out } #varfun <- function(v) dplyr::bind_rows(lapply(seq_len(v$natts), function(iatt) nc_att(x, v$name, iatt - 1)))) #' @name nc_atts #' @export nc_atts.character <- function(x, variable = NULL, ...) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_atts(nc, variable = variable) } ncmeta/R/nc_axis.R0000644000176200001440000000126414520356511013476 0ustar liggesusers#' NetCDF axes #' #' An `axis` is an instance of a dimension. #' #' Each data source has a set of dimensions available for use by variables. Each axis is #' a 1-dimensional instance. #' #' @param x NetCDF source #' @param i index of axis (1-based, 0 is "empty") #' #' @name nc_axis #' @export nc_axis <- function(x, i) { UseMethod("nc_axis") } #' @name nc_axis #' @export nc_axis.character <- function(x, i) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_axis(nc, i) } #' @name nc_axis #' @export nc_axis.NetCDF <- function(x, i) { nc_axes(x) %>% dplyr::filter(.data$axis == i) } ncmeta/R/utils.R0000644000176200001440000000447014520356511013214 0ustar liggesusers## https://gist.github.com/mdsumner/c086a5005c59373f4965fa6afd0d5a7c#gistcomment-2132051 # fast_tibble <- function(x) { # stopifnot(length(unique(unlist(lapply(x, length)))) == 1L) # structure(x, class = c("tbl_df", "tbl", "data.frame"), row.names = as.character(seq_along(x[[1]]))) # } # use with caution! this will cause problems if a ragged list is given ... faster_as_tibble <- function(x) { ## stopifnot(length(unique(lengths(x))) == 1L) structure(x, class = c("tbl_df", "tbl", "data.frame"), row.names = as.integer(seq_along(x[[1]]))) } split_fast_tibble <- function (x, f, drop = FALSE, ...) lapply(split(x = seq_len(nrow(x)), f = f, ...), function(ind) faster_as_tibble(lapply(x, "[", ind))) we_are_raady <- function() { fp <- getOption("default.datadir") #print(fp) stat <- FALSE if (!is.null(fp) && file.exists(file.path(fp, "data"))) stat <- TRUE stat } # This is a silly little function, but it can be useful. #' Find NetCDF Variable by attribute #' @description Given an attribute name and potentially a value, #' searches for and returns variables with the desired attribute. #' #' @param x open NetCDF object, or character file path or url to be opened with #' RNetCDF::open.nc #' @param attribute character the attribute name to search for variables with #' @param value character defaults to any only return variables that have the #' attribute with the given value #' @param strict boolean if TRUE, only exact matches of value will be returned #' #' @noRd #' #' @examples #' nc <- system.file("extdata/metdata.nc", package = "intersectr") #' #' find_var_by_att(nc, "coordinates") #' #' find_var_by_att(nc, "units") #' #' find_var_by_att(nc, "units", "degrees", strict = FALSE) #' #' find_var_by_att(nc, "units", "degrees", strict = TRUE) #' #' find_var_by_att(nc, "units", "degrees_east", strict = TRUE) #' find_var_by_att <- function(x, attribute, value = ".*", strict = TRUE) { open_nc <- FALSE if (is.character(x)) { x <- RNetCDF::open.nc(x) open_nc <- TRUE } if (inherits(x, "NetCDF")) { atts <- nc_atts(x) } else if (inherits(x, "data.frame")) { atts <- x } if (strict) value <- paste0("^", value, "$") atts <- atts[atts$name == attribute, ] atts <- atts[grepl(value, atts$value), ] if (open_nc) RNetCDF::close.nc(x) return(atts$variable) } ncmeta/R/nc_var.R0000644000176200001440000000221114520356511013313 0ustar liggesusers#' NetCDF variable #' #' Return a data frame about the variable at index `i`. #' @param x file name or handle #' @param i variable index (zero based) #' @name nc_var #' @return data frame of variable information #' @seealso `nc_vars` to obtain information about all variables, `nc_inq` for an #' overview of the file #' @export nc_var <- function(x, i) { UseMethod("nc_var") } #'@name nc_var #'@export nc_var.character <- function(x, i) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_var(nc, i) } #'@name nc_var #'@export nc_var.NetCDF <- function(x, i) { out <- RNetCDF::var.inq.nc(x, i) # Convert dimids to empty vector when variable is scalar: if (anyNA(out$dimids)) { out$dimids <- integer(0) } # Store vector values as nested lists: vectors <- c("dimids", "chunksizes", "filter_id", "filter_params") listcols <- vectors[vectors %in% names(out)] out[listcols] <- lapply(out[listcols], list) # Keep only list items with length 1: out <- out[lengths(out) == 1] # Transform list to tibble row: tibble::as_tibble_row(out) } ncmeta/R/nc_coord.R0000644000176200001440000001671314520356511013645 0ustar liggesusers#' Get Coordinate Variables for Given Variable #' #' In NetCDF, variables are defined along dimensions and are said to have "coordinate #' variables" that define the (typically spatio-temporal) positions of the data's cells. #' #' This function attempts to identify the X, Y, Z, and T coordinate variables for each #' data variable in the provided NetCDF source. The NetCDF-CF attribute conventions are #' used to make this determination. #' #' All variables that can be related to a spatio-temporal axis, including coordinate #' variables are returned. For coordinate variables, a "bounds" column is included in #' the response indicating which variable contains bounds information. #' #' See \url{http://cfconventions.org/cf-conventions/v1.6.0/cf-conventions.html#coordinate-system} #' for more. #' #' @return tibble with "variable", "X", "Y", "Z", "T", and "bounds" columns that reference #' variables by name. #' #' @name nc_coord_var #' @export #' @examples #' f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") #' nc_coord_var(f, "chlor_a") #' #' f <- system.file("extdata", "guam.nc", package = "ncmeta") #' nc_coord_var(f) nc_coord_var <- function(x, variable = NULL, ...) UseMethod("nc_coord_var") #' @param x NetCDF source #' @param variable variable name of interest. #' If not included, all variables will be returned. #' @param ... ignored #' #' @name nc_coord_var #' @export nc_coord_var.character <- function(x, variable = NULL, ...) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_coord_var_call(nc_dims(nc), nc_vars(nc), nc_atts(nc), nc_axes(x), variable) } #' @name nc_coord_var #' @export nc_coord_var.NetCDF <- function(x, variable = NULL, ...) { nc_coord_var_call(nc_dims(x), nc_vars(x), nc_atts(x), nc_axes(x), variable) } #' @importFrom dplyr bind_rows nc_coord_var_call <- function(dim, var, att, axe, variable) { if (is.null(var) || (nrow(var) < 1 & nrow(dim) < 1)) return(tibble::tibble()) if(is.null(variable)) { bind_rows(lapply(var$name, function(v) nc_coord_var_finder(dim, var, att, axe, v))) } else { nc_coord_var_finder(dim, var, att, axe, variable) } } #' @importFrom dplyr bind_rows filter select left_join group_by arrange mutate ungroup distinct nc_coord_var_finder <- function(dim, var, att, axe, variable) { if(nrow(att) == 0) return(NULL) v_atts <- att$variable == variable v_atts <- filter(att, v_atts) aux = FALSE if ("coordinates" %in% v_atts$name) { # NetCDF-CF introduces a "coordinates" attribute coordinates_atts <- filter(v_atts, name == "coordinates") coord_vars <- strsplit(coordinates_atts[["value"]][[1]], " ")[[1]] coord_vars <- coord_vars[nchar(coord_vars) > 0] if(!any(coord_vars %in% var$name)) { warning(paste("missing coordinate variables names in coordinates attribute", "trying to find non-auxiliary coordinate variables.")) } else { aux = TRUE } } # COARDS style coordinate variables have the same name as a dimension. v_dims <- axe$dimension[axe$variable == variable] v_dims <- dim$name[dim$id %in% v_dims] if(!aux) { if(length(v_dims) == 0) return(NULL) if(any(v_dims %in% var$name)) { coord_vars <- v_dims[v_dims %in% var$name] } else { return(NULL) } } else { if(any(v_dims %in% var$name)) { coord_vars <- unique(c(coord_vars, v_dims[v_dims %in% var$name])) } } coord_var <- sapply(coord_vars, divine_XYZT, atts = filter(att, variable %in% coord_vars), simplify = FALSE) coord_var <- coord_var[!sapply(coord_var, is.null)] if(length(coord_var) == 0) { return(NULL) } else { coord_var_base <- tibble::as_tibble(list(coord_var = names(coord_var), axis = unlist(coord_var))) out <- tibble::tibble(variable = character(0), X = character(0), Y = character(0), Z = character(0), T = character(0)) # coordinate variables not named in a coordinates attribute relate # by a shared dimension. First we need to get their dimension joined in. coord_var <- coord_var_base %>% left_join(select(axe, -axis), by = c("coord_var" = "variable")) # Now we can build up a table that relates data variables to # coordinate variables. coord_var <- tibble::as_tibble(list(variable = variable)) %>% left_join(select(axe, -axis), by = "variable") %>% left_join(coord_var, by = "dimension") %>% filter(!is.na(coord_var)) %>% select(variable, axis, coord_var) %>% distinct() out <-tryCatch({ bind_rows(out, coord_var %>% tidyr::spread(key = axis, value = coord_var)) }, error = function(e) { # Takes care of the case where there are both normal and auxiliary coordinate variables. bind_rows(out, filter(coord_var, !coord_var %in% dim$name) %>% tidyr::spread(key = axis, value = coord_var), filter(coord_var, coord_var %in% dim$name) %>% tidyr::spread(key = axis, value = coord_var)) }) bounds <- get_bounds(att) if(nrow(bounds) > 0) { out <- left_join(out, bounds, by = "variable") } else { if(nrow(out) > 0) { out$bounds <- NA_character_ } else { out$bounds <- character(0) } } return(out) } } axis <- variable <- name <- value <- NULL divine_XYZT <- function(var, atts) { att_sub <- filter(atts, variable == var) att_sub <- stats::setNames(att_sub$value, att_sub$name) # By units is preferred by COARDS lon_units <- c("degrees_east|degree_east|degree_E|degrees_E|degreeE|degreesE") if(!is.null(att_sub[["units"]]) && grepl(lon_units, att_sub[["units"]], ignore.case = TRUE)) return("X") lat_units <- "degrees_north|degree_north|degree_N|degrees_N|degreeN|degreesN" if(!is.null(att_sub[["units"]]) && grepl(lat_units, att_sub[["units"]], ignore.case = TRUE)) return("Y") # lat/lon by standard name if(!is.null(att_sub[["standard_name"]]) && grepl("longitude", att_sub[["standard_name"]], ignore.case = TRUE)) return("X") if(!is.null(att_sub[["standard_name"]]) && grepl("latitude", att_sub[["standard_name"]], ignore.case = TRUE)) return("Y") if(!is.null(att_sub[["standard_name"]]) && grepl("time", att_sub[["standard_name"]], ignore.case = TRUE)) return("T") # X/Y/Z/T by Axis if(!is.null(att_sub[["axis"]])) return(att_sub[["axis"]]) if(!is.null(att_sub[["positive"]])) return("Z") if(!is.null(att_sub[["units"]]) && grepl("since", att_sub[["units"]])) return("T") if(any(grepl("x coordinate of projection", att_sub)) | any(grepl("projection_x_coordinate", att_sub))) return("X") if(any(grepl("y coordinate of projection", att_sub)) | any(grepl("projection_y_coordinate", att_sub))) return("Y") } #' @importFrom rlang .data get_bounds <- function(atts) { dplyr::filter(atts, grepl("bounds", atts$name, ignore.case = TRUE)) %>% dplyr::select(variable, bounds = value) %>% dplyr::mutate(bounds = as.character(.data$bounds)) } ncmeta/R/nc_grid.R0000644000176200001440000000557614520356511013471 0ustar liggesusers#' NetCDF grids #' #' A `grid` is a discretized space, defined by a set of dimensions. These are the spaces used #' by one or more variables in a source. Traditional summaries are organized by variable, but #' when organized by space or grid we can treat multiple variables together using standard #' database techniques. #' #' Each data source has a set of dimensions available for use by variables. Each grid is #' an n-dimensional space available for use by 0, 1 or more variables. A grid only really #' exists if variable is defined for it, and 'grid' is an implicit entity not an explicit #' part of the NetCDF API definition. The Unidata pages refer to "shape", which is more or less what #' we mean by "grid". #' @name nc_grids #' @export nc_grids <- function(x, ...) UseMethod("nc_grids") #' @param x NetCDF source #' #' @param ... ignored #' #' @name nc_grids #' @export nc_grids.character <- function(x, ...) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_grids_dimvar(nc_dims(nc), nc_vars(nc), nc_axes(nc)) } #' @name nc_grids #' @export #' @importFrom dplyr %>% arrange group_by #' @importFrom tibble tibble nc_grids.NetCDF <- function(x, ...) { nc_grids_dimvar(nc_dims(x), nc_vars(x), nc_axes(x)) } # nc_vars(f) ## should be distinct # nc_axes(f) ## all of them # nc_axes(f, var) ## just these ones # nc_axis(i) ## just one, of all ?? expand_var <- function(x) { nc_axes(x) %>% dplyr::inner_join(nc_dims(x), c("dimension" = "id")) %>% dplyr::inner_join(nc_vars(x), c("variable" = "name")) } #' @importFrom dplyr desc arrange #' @importFrom rlang .data nc_grids_dimvar <- function(dimension, variable, axes) { if (is.null(variable) || (nrow(variable) < 1 & nrow(dimension) < 1)) return(tibble::tibble()) native_var <- unique(axes$variable) shape_instances_byvar <- split(axes$dimension, axes$variable)[native_var] shape_classify_byvar <- factor(unlist(lapply(shape_instances_byvar, function(xb) paste(paste0("D", xb), collapse = ",")))) out <- tibble::as_tibble(list(variable = names(shape_classify_byvar), grid = levels(shape_classify_byvar)[shape_classify_byvar])) out <- dplyr::arrange(out, dplyr::desc(nchar(.data$grid)), .data$grid) ## catch the NA shapes (the scalars) and set to "-" out[["grid"]][is.na(out[["grid"]]) | out[["grid"]] == "DNA"] <- "S" out[["ndims"]] <- unlist(lapply(strsplit(out$grid, ","), length)) # out %>% # dplyr::group_by(.data$grid, .data$ndims) %>% # dplyr::summarize(nvars = dplyr::n()) %>% # dplyr::ungroup() if (utils::packageVersion("tidyr") > "0.8.3" ) { nout <- tidyr::nest(out, variables = c(variable)) } else { nout <- tidyr::nest(out, .data$variable, .key = "variables") } nout$nvars <- unlist(lapply(nout$variables, nrow)) nout } ncmeta/R/nc_dimension.R0000644000176200001440000000145514520356511014521 0ustar liggesusers#' NetCDF dimension #' #' Get information about the dimensions in a NetCDF source. #' @param x file address or handle #' @param ... ignored #' #' @name nc_dims #' @export nc_dims <- function(x, ...) { UseMethod("nc_dims") } #' @name nc_dims #' @export nc_dims.character <- function(x, ...) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_dims(nc) } #' @name nc_dims #' @export nc_dims.NetCDF <- function(x, ...) { dplyr::bind_rows(lapply(seq_len(nc_inq(x)$ndims), function(i) nc_dim(x, i-1))) } nc_dims_internal <- function(x, ndims, ...) { dplyr::bind_rows(lapply(seq_len(ndims), function(i) nc_dim(x, i-1))) } #' @name nc_dims #' @export nc_dims.ncdf4 <- function(x, ...) { nc_dims(x$filename) } ncmeta/R/nc_axes.R0000644000176200001440000000375414520356511013500 0ustar liggesusers#' NetCDF axes #' #' An `axis` is an instance of a dimension. #' #' Each data source has a set of dimensions available for use by variables. Each axis is #' a 1-dimensional instance. #' #' @param x NetCDF source #' @param variables names of vars to query #' @param ... ignored #' #'@name nc_axes #'@export nc_axes <- function(x, variables = NULL, ...) { UseMethod("nc_axes") } #'@name nc_axes #'@export nc_axes.character <- function(x, variables = NULL, ...) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_axes(nc, variables = variables, ...) } #'@name nc_axes #'@export #'@importFrom dplyr row_number transmute #'@importFrom rlang .data nc_axes.NetCDF <- function(x, variables = NULL, ...) { if (is.null(variables)) { vars_to_query <- nc_vars(x) if (nrow(vars_to_query) < 1L) return(tibble()) variables <- vars_to_query$name } axes <- dplyr::bind_rows( lapply(variables, function(variable) { nc_axis_var(x, variable) }) ) ## if no dims, then it's not an axis see https://github.com/r-spatial/stars/pull/399 axes <- dplyr::filter(axes, .data$ndims > 0) # axes$id <- seq_len(nrow(axes)) ## row_number wtf #dplyr::transmute(axes, axis = row_number(), variable = .data$name, dimension = .data$dimids) tibble::as_tibble(list(axis = seq_len(nrow(axes)), variable = axes[["name"]], dimension = axes[["dimids"]])) } ## note this is a bit weird, but we have to ensure ## we work relative to all axes, so use the hidden function nc_axis_var nc_axis_var <- function(x, i) { out <- RNetCDF::var.inq.nc(x, i)[c("name", "ndims", "dimids")] #dimids <- out$dimids out[sapply(out, is.null)] <- NA ## as_tibble expands each vector to the length of the longest one ## which is what we want here longest <- max(lengths(out)) if (longest > 1L) out <- lapply(out, function(a) rep_len(a, length.out = longest)) out <- out[lengths(out) > 0] tibble::as_tibble(out) } ncmeta/R/ncmeta-package.r0000644000176200001440000000371714520356641014763 0ustar liggesusers#' @keywords internal "_PACKAGE" ## usethis namespace: start ## usethis namespace: end NULL #' ncmeta: straightforward NetCDF metadata #' #' `ncmeta` provides a consistent set of tools to obtain metadata from NetCDF. NetCDF #' is 'Network Common Data Form' https://www.unidata.ucar.edu/software/netcdf/. #' These functions are generics, allowing methods to be written for various providers so that #' everything can work from a common basis. All functions return a data frame. #' #' Each function responds to a character file name or data source (i.e. URL) or to a connection of a #' given class, this is so a source connection may be created a minimal number of times and kept open #' while a number of entities are queried. #' #' Each "given" entity may be referred to by index (0-based) or name, just as it would be by the NetCDF #' API and by the two R wrapper providers `RNetCDF` and `ncdf4`. #' \tabular{ll}{ #' \code{\link{nc_att}} \tab find the given attribute of a given variable \cr #' \code{\link{nc_atts}} \tab find all attributes, of all variables and globals \cr #' \code{\link{nc_axes}} \tab find all the instances of dimensions \cr #' \code{\link{nc_axis}} \tab find given instance of a dimension (1-based) \cr #' \code{\link{nc_dim}} \tab find the given dimension of a source (0-based) \cr #' \code{\link{nc_dims}} \tab find all the dimensions of a source \cr #' \code{\link{nc_grids}} \tab find the grids (sets of dimensions) of a source \cr #' \code{\link{nc_inq}} \tab inquire about a source (i.e. number of dimensions, number of variables, number of global attributes and presence of unlimited dimension \cr #' \code{\link{nc_meta}} \tab find all metadata for a source (runs all other functions) \cr #' \code{\link{nc_sources}} \tab tags a record of a source and its "access time" \cr #' \code{\link{nc_var}} \tab find a given variable (0-based) \cr #' \code{\link{nc_vars}} \tab find the variables of a source \cr #' } #' @name ncmeta-package NULL ncmeta/R/nc_inq_file.R0000644000176200001440000000242214520356511014315 0ustar liggesusers# nc_meta <- function(x) { # nc_vars(x) # nc_dims(x) # # } #' File info #' #' Get information about a NetCDF data source, may be a file path, or a `RNetCDF` #' file handle, or an OpenDAP/Thredds server address. #' @param x filename or handle #' @param ... ignored #' #' @export #' @examples #' \donttest{ #' \dontrun{ #' f <- raadfiles:::cmip5_files()$fullname[1] #' nc_inq(f) #' nc_var(f, 0) #' nc_dim(f, 0) #' } #' } #' f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") #' nc_inq(f) #' nc_var(f, 0) #' nc_dim(f, 0) #' #' nc_vars(f) #' nc_dims(f) nc_inq <- function(x, ...) { UseMethod("nc_inq") } #' @name nc_inq #' @export #' @importFrom RNetCDF file.inq.nc #' @importFrom tibble as_tibble nc_inq.NetCDF <- function(x, ...) { tibble::as_tibble(RNetCDF::file.inq.nc(x)) } #' @name nc_inq #' @export #' @importFrom dplyr bind_rows nc_inq.character <- function(x, ...) { ifun <- function(x) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_inq(nc) } out <- dplyr::bind_rows( lapply(x, ifun), .id = "filename") out$filename <- x[as.integer(out$filename)] out[, c("ndims", "nvars", "ngatts", "unlimdimid", "filename")] } ncmeta/R/nc_dim.R0000644000176200001440000000144414520356511013303 0ustar liggesusers#' NetCDF variables #' Obtain information about a single dimension by index. #' @param x filename or handle #' @param ... ignored #' @param i index of dimension (zero based) #' #' @name nc_dim #' @seealso `nc_vars` to obtain information about all dimensions, `nc_inq` for an #' overview of the file #' @export nc_dim <- function(x, i, ...) { UseMethod("nc_dim") } #'@name nc_dim #'@export nc_dim.character <- function(x, i, ...) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_dim(nc, i) } #'@name nc_dim #'@export nc_dim.NetCDF <- function(x, i, ...) { tibble::as_tibble(RNetCDF::dim.inq.nc(x, i)) } #'@name nc_dim #'@export nc_dim.ncdf4 <- function(x, i, ...) { nc_dim(x$filename, i, ...) } ncmeta/R/nc-gridmapping.R0000644000176200001440000003006314520356511014750 0ustar liggesusers#' Get Grid Mapping #' #' @description Get the grid mapping from a NetCDF file #' #' @return tibble containing attributes that make up the file's grid_mapping. #' A data_variable column is included to indicate which data variable the grid #' mapping belongs to. #' @export #' #' @name nc_grid_mapping_atts #' @examples #' #' nc_grid_mapping_atts(system.file("extdata/daymet_sample.nc", package = "ncmeta")) nc_grid_mapping_atts <- function(x, data_variable = NULL) UseMethod("nc_grid_mapping_atts") #' @param x open NetCDF object, character file path or url to be #' opened with RNetCDF::open.nc, or data.frame as returned from ncmeta::nc_atts #' #' @param data_variable character variable of interest #' #' @name nc_grid_mapping_atts #' @export nc_grid_mapping_atts.character <- function(x, data_variable = NULL) { nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) nc_grid_mapping_atts(nc, data_variable) } #' @name nc_grid_mapping_atts #' @export nc_grid_mapping_atts.NetCDF <- function(x, data_variable = NULL) { nc_grid_mapping_atts(nc_atts(x), data_variable) } #' @name nc_grid_mapping_atts #' @export nc_grid_mapping_atts.data.frame <- function(x, data_variable = NULL) { gm_att <- "grid_mapping" if(is.null(data_variable)) { data_variable <- find_var_by_att(x, gm_att) } else if(!gm_att %in% dplyr::filter(x, variable == data_variable)$name) { warning(paste("no grid_mapping attribute found for this variable")) return(tibble::tibble()) } if (length(data_variable) == 0 ) { warning(paste("No variables with a grid mapping found.\n", "Defaulting to WGS84 Lon/Lat")) return(tibble::as_tibble(list(name = c("grid_mapping_name", "semi_major_axis", "inverse_flattening", "longitude_of_prime_meridian"), value = list("latitude_longitude", 6378137, 298.257223563, 0)))) } grid_mapping_vars <- dplyr::filter(x, variable %in% data_variable & name %in% gm_att) %>% dplyr::mutate(value = as.character(value)) grid_mapping_atts <- dplyr::filter(x, variable %in% grid_mapping_vars$value) grid_mapping_atts <- dplyr::left_join(grid_mapping_atts, select(grid_mapping_vars, data_variable = variable, value = value), by = c("variable" = "value")) return(grid_mapping_atts) } #' Get NetCDF-CF grid mapping from projection #' #' Takes a proj4 string and returns a NetCDF-CF projection as #' a named list of attributes. #' #' @param prj character PROJ string as used in raster, sf, sp, proj4, and rgdal packages. #' #' @return A named list containing attributes required for that grid_mapping. #' #' #' @references #' \enumerate{ #' \item \url{https://en.wikibooks.org/wiki/PROJ.4} #' \item \url{https://trac.osgeo.org/gdal/wiki/NetCDF_ProjectionTestingStatus} #' \item \url{http://cfconventions.org/cf-conventions/cf-conventions.html#appendix-grid-mappings} #' } #' #' @export #' #' @examples #' prj <- "+proj=longlat +datum=NAD27 +no_defs" #' nc_prj_to_gridmapping(prj) #' p1 <- "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96" #' p2 <- "+x_0=0 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs" #' prj2 <- sprintf("%s %s", p1, p2) #' nc_prj_to_gridmapping(prj2) #' #' nc_prj_to_gridmapping("+proj=longlat +a=6378137 +f=0.00335281066474748 +pm=0 +no_defs") #' nc_prj_to_gridmapping <- function(prj) { al <- prepCRS(prj) if(is.null(al)) { return(tibble::as_tibble(list(name = character(0), value = list()))) } else { gm <- GGFP(al) return(tibble::as_tibble(list(name = names(gm), value = unname(gm)))) } } GGFP <- function(al) UseMethod("GGFP") GGFP.latitude_longitude <- function(al) { gm <- c(list(grid_mapping_name = "latitude_longitude"), getGeoDatum_gm(al)) gm } GGFP.albers_conical_equal_area <- function(al) { gm <- c(list(grid_mapping_name = "albers_conical_equal_area"), lonCentMer_gm(al), latProjOrig_gm(al), falseEastNorth_gm(al), standPar_gm(al), getGeoDatum_gm(al)) gm } GGFP.azimuthal_equidistant <- function(al) { gm <- c(list(grid_mapping_name = "azimuthal_equidistant"), lonProjOrig_gm(al), latProjOrig_gm(al), falseEastNorth_gm(al), getGeoDatum_gm(al)) gm } GGFP.lambert_azimuthal_equal_area <- function(al) { gm <- c(list(grid_mapping_name = "lambert_azimuthal_equal_area"), latProjOrig_gm(al), lonProjOrig_gm(al), falseEastNorth_gm(al), getGeoDatum_gm(al)) gm } GGFP.lambert_conformal_conic <- function(al) { gm <- c(list(grid_mapping_name = "lambert_conformal_conic"), standPar_gm(al), falseEastNorth_gm(al), latProjOrig_gm(al), lonCentMer_gm(al), getGeoDatum_gm(al)) gm } GGFP.lambert_cylindrical_equal_area <- function(al) { gm <- c(list(grid_mapping_name = "lambert_cylindrical_equal_area"), lonCentMer_gm(al), oneStandPar_gm(al), falseEastNorth_gm(al), getGeoDatum_gm(al)) gm } GGFP.mercator <- function(al) { if(!is.null(al$k)) { gm <- c(list(grid_mapping_name = "mercator"), lonProjOrig_gm(al), scaleFactorProjOrig_gm(al), falseEastNorth_gm(al), getGeoDatum_gm(al)) } else { gm <- c(list(grid_mapping_name = "mercator"), lonProjOrig_gm(al), oneStandPar_gm(al), falseEastNorth_gm(al), getGeoDatum_gm(al)) } gm } GGFP.oblique_mercator <- function(al) { #!!!! Check this one out. the oMerc function is a hack !!!! gm <- c(list(grid_mapping_name = "oblique_mercator"), latProjOrig_gm(al), lonProjCent_gm(al), scaleFactorProjOrig_gm(al), oMerc_gm(al), falseEastNorth_gm(al), getGeoDatum_gm(al)) gm } GGFP.orthographic <- function(al) { gm <- c(list(grid_mapping_name = "orthographic"), latProjOrig_gm(al), lonProjOrig_gm(al), falseEastNorth_gm(al), getGeoDatum_gm(al)) gm } # GGFP.polar_stereographic <- function(al) { # if(!is.null(al$k)) { # gm <- c(list(grid_mapping_name = "polar_stereographic"), # latProjOrig_gm(al), # stVertLon_gm(al), # scaleFactorProjOrig_gm(al), # falseEastNorth_gm(al), # getGeoDatum_gm(al)) # } else { # gm <- c(list(grid_mapping_name = "polar_stereographic"), # latProjOrig_gm(al), # stVertLon_gm(al), # oneStandPar_gm(al), # falseEastNorth_gm(al), # getGeoDatum_gm(al)) # } # gm # } # GGFP.sinusoidal <- function(al) { # gm <- c(list(grid_mapping_name = "sinusoidal"), # lonProjOrig_gm(al), # falseEastNorth_gm(al), # getGeoDatum_gm(al)) # gm # } GGFP.stereographic <- function(al) { gm <- c(list(grid_mapping_name = "stereographic"), latProjOrig_gm(al), lonProjOrig_gm(al), scaleFactorProjOrig_gm(al), falseEastNorth_gm(al), getGeoDatum_gm(al)) gm } GGFP.transverse_mercator <- function(al) { gm <- c(list(grid_mapping_name = "transverse_mercator"), latProjOrig_gm(al), lonCentMer_gm(al), scaleFactorCentMer_gm(al), falseEastNorth_gm(al), getGeoDatum_gm(al)) gm } lonCentMer_gm <- function(al) { list(longitude_of_central_meridian = as.numeric(al$lon_0)) } latProjOrig_gm <- function(al) { list(latitude_of_projection_origin = as.numeric(al$lat_0)) } lonProjOrig_gm <- function(al) { list(longitude_of_projection_origin = as.numeric(al$lon_0)) } falseEastNorth_gm <- function(al) { list(false_easting = as.numeric(al$x_0), false_northing = as.numeric(al$y_0)) } standPar_gm <- function(al) { if(al$lat_1 != al$lat_2) { list(standard_parallel = c(as.numeric(al$lat_1), as.numeric(al$lat_2))) } else if(al$lat_1 == al$lat_2) { list(standard_parallel = as.numeric(al$lat_1)) } } oneStandPar_gm <- function(al) { list(standard_parallel = c(as.numeric(al$lat_ts))) } getGeoDatum_gm <- function(al) { if(!is.null(al$datum) && al$datum == "NAD83") { list(semi_major_axis = 6378137, inverse_flattening = 298.257222101, longitude_of_prime_meridian = 0) } else if(!is.null(al$datum) && al$datum == "WGS84") { list(semi_major_axis = 6378137, inverse_flattening = 298.257223563, longitude_of_prime_meridian = 0) } else if(!is.null(al$datum) && al$datum == "NAD27") { list(semi_major_axis = 6378206.4, inverse_flattening = 294.978698214, longitude_of_prime_meridian = 0) } else if(!is.null(al$ellps) && !is.null(al$towgs84) && al$towgs84 == "0,0,0,0,0,0,0") { list(semi_major_axis = 6378137, inverse_flattening = 298.257223563, longitude_of_prime_meridian = 0) } else if(!is.null(al$a) && !is.null(al$f) && !is.null(al$pm)) { list(semi_major_axis = as.numeric(al$a), inverse_flattening = (1/as.numeric(al$f)), longitude_of_prime_meridian = as.numeric(al$pm)) } else if(!is.null(al$a) && !is.null(al$b) && !is.null(al$pm)) { list(semi_major_axis = as.numeric(al$a), semi_minor_axis = as.numeric(al$b), longitude_of_prime_meridian = as.numeric(al$pm)) } else { warning("no datum information found assuming WGS84") list(semi_major_axis = 6378137, inverse_flattening = 298.257223563, longitude_of_prime_meridian = 0) } } scaleFactorCentMer_gm <- function(al) { list(scale_factor_at_central_meridian = as.numeric(al$k)) } scaleFactorProjOrig_gm <- function(al) { list(scale_factor_at_projection_origin = as.numeric(al$k)) } oMerc_gm <- function(al) { list(azimuth_of_central_line = as.numeric(al$alpha)) } lonProjCent_gm <- function(al) { list(longitude_of_projection_origin = as.numeric(al$lonc)) } check_args <- function (x) { ## FIXME: checks as in reproj stop("cannot convert from digits, did you enter an EPSG code?") if (is.numeric(x) || (nchar(x) %in% c(4, 5) && grepl("^[0-9]{1,5}$", x))) { return(FALSE) } if (!substr(x, 1, 1) == "+") return(FALSE) TRUE } prepCRS <- function(prj) { if(inherits(prj, "CRS")) prj <- prj@projargs if(!check_args(prj)[1][[1]]) { warning("not a valid crs, returning an empty tibble") return(NULL) } args <- unique(unlist(strsplit(prj, " "))) argList <- list() for(arg in args) { a <- unlist(strsplit(sub("\\+", "", arg), "=")) argList[a[1]] <- a[2] } cf_proj_lookup <- list(aea = "albers_conical_equal_area", aeqd = "azimuthal_equidistant", laea = "lambert_azimuthal_equal_area", lcc = "lambert_conformal_conic", cea = "lambert_cylindrical_equal_area", longlat = "latitude_longitude", merc = "mercator", omerc = "oblique_mercator", ortho = "orthographic", stere = "stereographic", tmerc = "transverse_mercator") class(argList) <- cf_proj_lookup[unlist(argList["proj"])][[1]] if(!class(argList) %in% cf_proj_lookup) { warning("no available mapping to netcdf projection, returning empty crs list") return(NULL) } else { return(argList) } } ncmeta/R/nc-prj.R0000644000176200001440000002112114520356511013235 0ustar liggesusers#' Get projection from NetCDF-CF Grid Mapping #' #' Takes NetCDF-CF grid mapping attributes and returns #' a proj4 string. #' #' The WGS84 datum is used as a default if one os not provided #' in the grid mapping. #' #' If only a semi_major axis is provided, a sperical earth is assumed. #' #' @param x list or data.frame of attributes of the grid mapping variable #' as returned by ncdf or ncdf4's get attributes functions or ncmeta's nc_grid_mapping_atts. #' #' @return A proj4 string. #' #' @references #' \enumerate{ #' \item \url{https://en.wikibooks.org/wiki/PROJ.4} #' \item \url{https://trac.osgeo.org/gdal/wiki/NetCDF_ProjectionTestingStatus} #' \item \url{http://cfconventions.org/cf-conventions/cf-conventions.html#appendix-grid-mappings} #' } #' #' @export #' @examples #' #' crs <- list(grid_mapping_name="latitude_longitude", #' longitude_of_prime_meridian = 0, #' semi_major_axis = 6378137, #' inverse_flattening = 298) #' nc_gm_to_prj(crs) nc_gm_to_prj <- function(x) UseMethod("nc_gm_to_prj") #' @name nc_gm_to_prj #' @export nc_gm_to_prj.data.frame <- function(x) { nc_gm_to_prj(stats::setNames(x$value, x$name)) } #' @name nc_gm_to_prj #' @export nc_gm_to_prj.list <- function(x) { class(x) <- x$grid_mapping_name GPFN(x) } GPFN <- function(gm) UseMethod("GPFN") GPFN.albers_conical_equal_area <- function(gm) { projargs <- paste("+proj=aea", standPar(gm), falseEastNorth(gm), latProjOrig(gm), lonCentMer(gm), getGeoDatum(gm)) } GPFN.azimuthal_equidistant <- function(gm) { projargs <- paste("+proj=aeqd", latProjOrig(gm), lonProjOrig(gm), falseEastNorth(gm), getGeoDatum(gm)) } # GPFN.geostationary <- function(gm) { # #+proj=geos +lon_0=0 +h=-0 +x_0=0 +y_0=0 +no_defs # projargs <- paste("+proj=geos", # latProjOrig(gm), # lonProjOrig(gm), # # persHeight(gm), # falseEastNorth(gm), # getGeoDatum(gm)) # # Fixed angle and sweep angle axes? # } GPFN.lambert_azimuthal_equal_area <- function(gm) { projargs <- paste("+proj=laea", latProjOrig(gm), lonProjOrig(gm), falseEastNorth(gm), getGeoDatum(gm)) } GPFN.lambert_conformal_conic <- function(gm) { projargs <- paste("+proj=lcc", standPar(gm), falseEastNorth(gm), latProjOrig(gm), lonCentMer(gm), getGeoDatum(gm)) } GPFN.lambert_cylindrical_equal_area <- function(gm) { projargs <- paste("+proj=cea", lonCentMer(gm), oneStandPar(gm), falseEastNorth(gm), getGeoDatum(gm)) } GPFN.latitude_longitude <- function(gm) { prj <- paste0("+proj=longlat ", getGeoDatum(gm)) } GPFN.mercator <- function(gm) { if(!is.null(gm$scale_factor_at_projection_origin)) { projargs <- paste("+proj=merc", lonProjOrig(gm), scaleFactorProjOrig(gm), falseEastNorth(gm), getGeoDatum(gm)) } else { projargs <- paste("+proj=merc", lonProjOrig(gm), oneStandPar(gm), falseEastNorth(gm), getGeoDatum(gm)) } } GPFN.oblique_mercator <- function(gm) { #!!!! Check this one out. the oMerc function is a hack !!!! projargs <- paste("+proj=omerc", latProjOrig(gm), lonProjCent(gm), scaleFactorProjOrig(gm), oMerc(gm), falseEastNorth(gm), getGeoDatum(gm)) } GPFN.orthographic <- function(gm) { projargs <- paste("+proj=ortho", latProjOrig(gm), lonProjOrig(gm), falseEastNorth(gm), getGeoDatum(gm)) } GPFN.polar_stereographic <- function(gm) { if(!is.null(gm$scale_factor_at_projection_origin)) { projargs <- paste("+proj=stere", latProjOrig(gm), stVertLon(gm), scaleFactorProjOrig(gm), falseEastNorth(gm), getGeoDatum(gm)) } else { projargs <- paste("+proj=stere", latProjOrig(gm), stVertLon(gm), oneStandPar(gm), falseEastNorth(gm), getGeoDatum(gm)) } } # GPFN.rotated_latitude_longitude <- function(gm) { # # not supported? # } # GPFN.sinusoidal <- function(gm) { projargs <- paste("+proj=sinu", lonProjOrig(gm), falseEastNorth(gm), getGeoDatum(gm)) } # GPFN.stereographic <- function(gm) { projargs <- paste("+proj=stere", latProjOrig(gm), lonProjOrig(gm), scaleFactorProjOrig(gm), falseEastNorth(gm), getGeoDatum(gm)) } GPFN.transverse_mercator <- function(gm) { projargs <- paste("+proj=tmerc", latProjOrig(gm), lonCentMer(gm), scaleFactorCentMer(gm), falseEastNorth(gm), getGeoDatum(gm)) } # # GPFN.vertical_perspective <- function(gm) { # #"+proj=nsper +h=1" # Not supported? # } getGeoDatum <- function(gm) { if(is.null(gm$longitude_of_prime_meridian)) { warning("Didn't find a longitude of prime meridian for datum, assuming 0.") gm$longitude_of_prime_meridian <- 0 } if(is.null(gm$semi_major_axis)) { if(!is.null(gm$earth_radius)) { warning("Didn't find a semi major axis but did find earth radius. Assuming spherical earth") gm$semi_major_axis <- gm$earth_radius gm$semi_minor_axis <- gm$earth_radius } else { warning("Didn't find a semi major axis for datum, assuming WGS84 6378137.0 meters") gm$semi_major_axis <- 6378137.0 if(is.null(gm$inverse_flattening) && is.null(gm$semi_minor_axis)) { warning("Didn't find an inverse flattening value, assuming WGS84 298.257223563") gm$inverse_flattening <- 298.257223563 } } } if(!is.null(gm$inverse_flattening) && gm$inverse_flattening == 0) { gm$inverse_flattening <- NULL gm$semi_minor_axis <- gm$semi_major_axis } if(!is.null(gm$inverse_flattening)) { geoDatum <- paste0("+a=", gm$semi_major_axis, " +f=", (1/gm$inverse_flattening), " +pm=", gm$longitude_of_prime_meridian, " +no_defs") } else if(!is.null(gm$semi_minor_axis)) { geoDatum <- paste0("+a=", gm$semi_major_axis, " +b=", gm$semi_minor_axis, " +pm=", gm$longitude_of_prime_meridian, " +no_defs") } else { geoDatum <- paste0("+a=", gm$semi_major_axis, " +b=", gm$semi_major_axis, " +pm=", gm$longitude_of_prime_meridian, " +no_defs") } return(geoDatum) } standPar <- function(gm) { if(length(gm$standard_parallel)==1) { gm$standard_parallel <- c(gm$standard_parallel, gm$standard_parallel) } outString <- paste0("+lat_1=", gm$standard_parallel[1], " +lat_2=", gm$standard_parallel[2]) } oneStandPar <- function(gm) { outString <- paste0("+lat_ts=", gm$standard_parallel[1]) } falseEastNorth <- function(gm) { options(scipen=2) outString <- paste0("+x_0=", gm$false_easting, " +y_0=", gm$false_northing, " +units=m") } latProjOrig <- function(gm) { outString <- paste0("+lat_0=", gm$latitude_of_projection_origin) } lonCentMer <- function(gm) { outString <- paste0("+lon_0=", gm$longitude_of_central_meridian) } lonProjOrig <- function(gm) { outString <- paste0("+lon_0=", gm$longitude_of_projection_origin) } stVertLon <- function(gm) { outString <- paste0("+lon_0=", gm$straight_vertical_longitude_from_pole) } scaleFactorCentMer <- function(gm) { outString <- paste0("+k=", gm$scale_factor_at_central_meridian) } scaleFactorProjOrig <- function(gm) { outString <- paste0("+k=", gm$scale_factor_at_projection_origin) } oMerc <- function(gm) { outString <- paste0("+alpha=", gm$azimuth_of_central_line, " +gamma=", gm$azimuth_of_central_line, " +no_uoff") } lonProjCent <- function(gm) { outString <- paste0("+lonc=", gm$longitude_of_projection_origin) } ncmeta/R/nc_meta.R0000644000176200001440000000362714520356511013465 0ustar liggesusers#' Top level NetCDF metadata. #' #' This function exists to maintain the open connection #' while all dimension, variable, and attribute metadata is extracted. #' #' This function is pretty ambitious, and will send nearly any string #' to the underlying NetCDF library other than "", which immediately #' generates an error. This should be robust, but might present fairly #' obscure error messages from the underlying library. #' @param ... ignored #' @param x data source address, file name or handle #' #' @export #' @examples #' f <- system.file("extdata", "S2008001.L3m_DAY_CHL_chlor_a_9km.nc", package = "ncmeta") #' nc_meta(f) #' \donttest{ #' \dontrun{ #' u <- "https://upwell.pfeg.noaa.gov/erddap/tabledap/FRDCPSTrawlLHHaulCatch" #' nc_meta(u) #' }} nc_meta <- function(x, ...) { if (missing(x)) stop("'x' must be a valid NetCDF source, filename or URL") UseMethod("nc_meta") } #' @name nc_meta #' @export nc_meta.NetCDF <- function(x, ...) { inq <- nc_inq(x) dims <- nc_dims_internal(x, inq[["ndims"]]) vars <- nc_vars_internal(x, inq$nvars) if (nrow(vars) > 1) axis <- nc_axes(x, vars$name) else axis <- nc_axes(x) ## does a dimension have dim-vals? if (nrow(dims) > 0) dims[["coord_dim"]] <- dims[["name"]] %in% vars[["name"]] ## is a variable a dim-val? if (nrow(vars) > 0) { vars[["dim_coord"]] <- vars[["ndims"]] == 1L & vars[["name"]] %in% dims[["name"]] } else { vars <- NULL ## avoid passing along a 0-row data frame } structure(list(dimension = dims, variable = vars, attribute = nc_atts(x), axis = axis, grid = nc_grids_dimvar(dims, vars, axis)), class = "ncmeta") } #' @name nc_meta #' @export nc_meta.character <- function(x, ...) { if (nchar(x) < 1) stop("NetCDF source cannot be empty string") nc <- RNetCDF::open.nc(x) on.exit(RNetCDF::close.nc(nc), add = TRUE) out <- nc_meta(nc) out$source <- nc_sources(x) out }ncmeta/R/nc_sources.R0000644000176200001440000000073114520356511014213 0ustar liggesusers#' NetCDF sources #' #' A record of file, URL, or any data source with NetCDF. #' @param x data source string #' #' @param ... ignored #' #' @name nc_sources #' @export nc_sources <- function(x, ...) { UseMethod("nc_sources") } #' @name nc_sources #' @export nc_sources.character <- function(x, ...) { if (file.exists(x)) { path <- normalizePath(x, winslash = "/") } else { path <- x } tibble(access = Sys.time(), source = path) } ncmeta/NEWS.md0000644000176200001440000000474514520356702012635 0ustar liggesusers# ncmeta 0.3.6 * Fixed namespace doc thanks to CRAN. # ncmeta 0.3.5 * Removed LazyData, unused. * Extra info now provided by RNetCDF was causing non-nested data frame problems, fixed by PR from @mjwoods in #44 and #45. * Changed default branch name to "main", so please check if you have a fork and submit PRs. * Fix bug where Scalar variables were treated as an axis. Picked up in stars PR #399. # ncmeta 0.3.0 * Fix file path mangling introduced in #27. # ncmeta 0.2.5 * Improved equivalence test thanks to Romain Francois #37. # ncmeta 0.2.0 * Simplified error handling when file not able to be opened. https://github.com/ropensci/tidync/issues/98 * Fixed bug in nc_atts() https://github.com/hypertidy/ncmeta/issues/36. # ncmeta 0.1.0 * Extra checks and fixes to align with stars, and a future release of RNetCDF. * Condition on version of tidyr for new `nest()` syntax > 0.8.3. * The output of `nc_atts()` is now more consistent, with the same structure given for only global attributes, or a mix of variable attributes and global attributes. If there are no attributes at all the output has zero rows, but now has the correct column types. # ncmeta 0.0.4 * `nc_grids()` now normal form, with nested variables so we can easily link grids to variables. * New functions `nc_coord_var` to find coordinate variables (if any), `gm_to_prj` to determine PROJ string in use, and `nc_grid_mapping_atts` to determine grid mapping parameters; #19, #14, #12, #9. # ncmeta 0.0.3 * Fix for grid organization providing variables out of native order. * Added 'variable' argument to 'nc_atts', per #8. # ncmeta 0.0.2 * added some extra checks for bad source strings, particularly the empty string to prevent crashing (this happens with `system.file()` where the file does not exist) * fix attribute types problem # ncmeta 0.0.1 *updates from CRAN feedback * ncmeta now provides support for dimensions that do not have explicit coordinates: dimension and variable tables now have information about "dimvals", in the form of "dim_coord" and "coord_dim" i.e. if a variable is 1D and its name corresponds to a dimension name, then it is a rectlinear coordinate vector of that dimension. 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*tests/testthat/test-gridmapping-prj.R d6daa8435b27b5edb4294cbc055f3192 *tests/testthat/test-tidync.R caedf44f8cf285b0bc2cab6f09e72bc2 *tests/testthat/test-utils.R 85237079a16c479fd35b045d5e66a427 *tests/testthat/test-vars.R ncmeta/inst/0000755000176200001440000000000014520363106012476 5ustar liggesusersncmeta/inst/extdata/0000755000176200001440000000000014520363106014130 5ustar liggesusersncmeta/inst/extdata/daymet_sample.nc0000644000176200001440000000405414520356511017303 0ustar liggesusersCDF timeyx start_yearsourceDaymet Software Version 3.0Version_softwareDaymet Software Version 3.0 Version_dataDaymet Data Version 3.0 ConventionsCF-1.6citationOPlease see http://daymet.ornl.gov/ for current Daymet data citation information referencesOPlease see http://daymet.ornl.gov/ for current information on Daymet referencestitlejDaymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3 (Continental North America) institutionKOak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)end_year prcp _CoordinateAxeslat lon time y x _FillValue< long_namedaily total precipitationunitsmm/day missing_value< coordinateslat lon grid_mappinglambert_conformal_conic cell_methodsarea: mean time: sum,time  long_nametimecalendarstandardunits"days since 1980-01-01 00:00:00 UTCbounds time_bnds_CoordinateAxisTypeTime0y unitskm long_namey coordinate of projection x unitskm long_namex coordinate of projection$lambert_conformal_conic grid_mapping_namelambert_conformal_coniclongitude_of_central_meridianYlatitude_of_projection_origin@E@ false_eastingfalse_northingstandard_parallel@9@Nsemi_major_axisAXT@inverse_flattening@rtmlongitude_of_prime_meridian_CoordinateTransformType Projection_CoordinateAxisTypes GeoX GeoY(||ncmeta/inst/extdata/rasterwise-timeseries.nc0000644000176200001440000000212414520356511021012 0ustar liggesusersCDF stationtime  featureType timeSeries ConventionsCF-1.7 num  long_nameStation numbercf_role timeseries_id<time units"days since 1970-01-01 00:00:00 UTC long_nametimecalendar gregorian@pr units kg m-2 s-1 _FillValue long_nameTotal precipitation flux coordinateslat lon alt num standard_nameprecipitation_fluxDlat units degrees_north long_nameStation latitude standard_namelatitudeHlon units degrees_east long_nameStation longitude standard_name longitudeLalt unitsm long_name#Vertical distance above the surface standard_nameheightP |||ncmeta/inst/extdata/avhrr-only-v2.19810901.cdl0000644000176200001440000000370614520356511020165 0ustar liggesusersnetcdf avhrr-only-v2.19810901 { dimensions: time = 1 ; zlev = 1 ; lat = 10 ; lon = 14 ; variables: float time(time) ; time:long_name = "Center time of the day" ; time:units = "days since 1978-01-01 00:00:00" ; float zlev(zlev) ; zlev:long_name = "Sea surface height" ; zlev:units = "meters" ; zlev:actual_range = "0, 0" ; float lat(lat) ; lat:long_name = "Latitude" ; lat:units = "degrees_north" ; lat:grids = "Uniform grid from -89.875 to 89.875 by 0.25" ; float lon(lon) ; lon:long_name = "Longitude" ; lon:units = "degrees_east" ; lon:grids = "Uniform grid from 0.125 to 359.875 by 0.25" ; short sst(time, zlev, lat, lon) ; sst:long_name = "Daily sea surface temperature" ; sst:units = "degrees C" ; sst:_FillValue = -999s ; sst:add_offset = 0.f ; sst:scale_factor = 0.01f ; sst:valid_min = -300s ; sst:valid_max = 4500s ; short anom(time, zlev, lat, lon) ; anom:long_name = "Daily sea surface temperature anomalies" ; anom:units = "degrees C" ; anom:_FillValue = -999s ; anom:add_offset = 0.f ; anom:scale_factor = 0.01f ; anom:valid_min = -1200s ; anom:valid_max = 1200s ; short err(time, zlev, lat, lon) ; err:long_name = "Estimated error standard deviation of analysed_sst" ; err:units = "degrees C" ; err:_FillValue = -999s ; err:add_offset = 0.f ; err:scale_factor = 0.01f ; err:valid_min = 0s ; err:valid_max = 1000s ; short ice(time, zlev, lat, lon) ; ice:long_name = "Sea ice concentration" ; ice:units = "percentage" ; ice:_FillValue = -999s ; ice:add_offset = 0.f ; ice:scale_factor = 0.01f ; ice:valid_min = 0s ; ice:valid_max = 100s ; // global attributes: :Conventions = "CF-1.0" ; :title = "Daily-OI-V2, final, Data (Ship, Buoy, AVHRR, GSFC-ice)" ; :History = "Version 2.0" ; :creation_date = "2011-05-04" ; :Source = "NOAA/National Climatic Data Center" ; :Contact = "Dick Reynolds, email: Richard.W.Reynolds@noaa.gov & Chunying Liu, email: Chunying.liu@noaa.gov" ; } ncmeta/inst/extdata/daymet_sample.cdl0000644000176200001440000000412414520356511017443 0ustar liggesusersnetcdf daymet { dimensions: time = 0 ; y = 1 ; x = 1 ; variables: float prcp(time, y, x) ; prcp:_CoordinateAxes = "lat lon time y x " ; prcp:_FillValue = -9999.f ; prcp:long_name = "daily total precipitation" ; prcp:units = "mm/day" ; prcp:missing_value = -9999.f ; prcp:coordinates = "lat lon" ; prcp:grid_mapping = "lambert_conformal_conic" ; prcp:cell_methods = "area: mean time: sum" ; double time(time) ; time:long_name = "time" ; time:calendar = "standard" ; time:units = "days since 1980-01-01 00:00:00 UTC" ; time:bounds = "time_bnds" ; time:_CoordinateAxisType = "Time" ; float y(y) ; y:units = "km" ; y:long_name = "y coordinate of projection" ; float x(x) ; x:units = "km" ; x:long_name = "x coordinate of projection" ; short lambert_conformal_conic ; lambert_conformal_conic:grid_mapping_name = "lambert_conformal_conic" ; lambert_conformal_conic:longitude_of_central_meridian = -100. ; lambert_conformal_conic:latitude_of_projection_origin = 42.5 ; lambert_conformal_conic:false_easting = 0. ; lambert_conformal_conic:false_northing = 0. ; lambert_conformal_conic:standard_parallel = 25., 60. ; lambert_conformal_conic:semi_major_axis = 6378137. ; lambert_conformal_conic:inverse_flattening = 298.257223563 ; lambert_conformal_conic:longitude_of_prime_meridian = 0. ; lambert_conformal_conic:_CoordinateTransformType = "Projection" ; lambert_conformal_conic:_CoordinateAxisTypes = "GeoX GeoY" ; // global attributes: :start_year = 1980s ; :source = "Daymet Software Version 3.0" ; :Version_software = "Daymet Software Version 3.0" ; :Version_data = "Daymet Data Version 3.0" ; :Conventions = "CF-1.6" ; :citation = "Please see http://daymet.ornl.gov/ for current Daymet data citation information" ; :references = "Please see http://daymet.ornl.gov/ for current information on Daymet references" ; :title = "Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 3 (Continental North America)" ; :institution = "Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC)" ; :end_year = 2015s ; } ncmeta/inst/extdata/S2008001.L3b_DAY_CHL.nc0000644000176200001440000020255514520356511017224 0ustar liggesusersHDF  m0-OHDR,} Vz V} Vz V'p &level-3_binned_data%processing_controlI1 M!#/C*OHDR,} V{ V} V{ V" %~&& ~4,4,H,,kOHDR { V{ V{ V{ VkԩOHDR-{ V{ V{ V{ V!  @ +CLASSDIMENSION_SCALE ZNAME@This is a netCDF dimension but not a netCDF variable. 0=OHDR-{ V{ V{ V{ V  0 LDIMENSION_LIST0aHPOCHK6bin_num nobsnscenesweights  time_rec   bOCHK l0REFERENCE_LIST6datasetdimension - 5OHDR { V{ V{ V{ VF|!W.g"8FSSE&1Z'taTypeNOHDR-{ V{ V{ V{ V!   +CLASSDIMENSION_SCALEh#&nQFSSE@ } FHjBTHDd(m]鬝OCHK ZNAME@This is a netCDF dimension but not a netCDF variable. 0UGCOLl``$TREEJ`(OHDR-{ V{ V{ V{ V  ( LDIMENSION_LIST0ŷ;{OCHKB6sum  sum_squared  liBTHDd(Q1FSSE]Yw֛OHDR } V} V} V} V['lC ,OHDR-} V{ V} V{ V  1 LDIMENSION_LIST0BTHD  d(U1-chl_ocx"B JOCHK |0REFERENCE_LIST6datasetdimension ` "կ4%BTHD  d(m_OHDR-} V} V} V} V!   +CLASSDIMENSION_SCALE'h_>z&+FFRHP&  >UBTHD d(C9 BTHDd(C; "FFSHDPx 4rOCHK ZNAME@This is a netCDF dimension but not a netCDF variable. 0UOCHKW6start_num begin extent max  PVOCHK":2x^! QA (g3d\u%2H͸" -,haW KZX>TREE90x^nft|,`o.2 "# HAY@]dd{tTREE9CABTLF, Hv1P)g93!@-XALv3_h sc4['j:?BTLF3Pj :QTOHDR-} V} V} V} Vp " |A LDIMENSION_LIST0;̗OCHK l0REFERENCE_LIST6datasetdimension C=fFHDB%(} binListType binListDimlBinList binDataType binDataDim`chlor_a` chl_ocx" binIndexType" binIndexDim$ BinIndexC=FSHDPx(|%%f#Dx^nft|,`o.2 "# HAY@]dd{tTREE mamqmmmmmmm OHDR,} V} V} V} V" #input_parameters{JJK  )software_namel2bin4COHDR,} V} V} V} V" \$\cOCHKY  ,software_version4.0.5 l2_flag_namesATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,CLDICE,COCCOLITH,LOWLW,CHLWARN,CHLFAIL,NAVWARN,MAXAERITER,ATMWARN,HISOLZEN,NAVFAIL,FILTER,HIGLINT%*CFHIB MmmmmN:FRHP@1(WL읖BTLFL7e K# ! ^ P. j/M: D)5[%F8o)~7{\/B@4%o5)B zT9(_/< e0N=|tF[.QH@4K1%R 0+TI nZGk] 3&Dbk@ cLqdd B"S{P7Qń:h7BTLF :P7(((@?>};/B )B k@ K K AKKLbLL\BTINSjNMSc,K# ! ^ P. j/M: D)5[%F8o)~7{\/B@4%o5)B zT9(_/< e0N=|tF[.QH@4K1%R 0+TI nZGk] 3&Dbk@ cLqdd B"S{P7Qń:h7BTLF A'~td@S(cdL H-wK=? H!0-;i(9BM/?>:zB z}#AK/E{~ F*i!bL3A C(k+ K SzLK w}; o$),~(7Qń:h7BTIN LOW;<4((@?>};/B )B k@ K K AKKLbLL\BTLF F8~7SS[.7G;B@M|dI ? H! B" K# o$1% 3& A' C(: D)~ F* 0+), H-^ P. j/ e0IOCHK sourceS2007365180135.L2_GAC_OC.nc,S2007365180901.L2_GAC_OC.nc,S2007365194235.L2_GAC_OC.nc,S2007365212117.L2_GAC_OC.nc,S2007365225958.L2_GAC_OC.nc,S2008001003832.L2_GAC_OC.nc,S2008001021713.L2_GAC_OC.nc,S2008001035555.L2_GAC_OC.nc,S2008001053447.L2_GAC_OC.nc,S2008001071318.L2_GAC_OC.nc,S2008001085200.L2_GAC_OC.nc,S2008001103041.L2_GAC_OC.nc,S2008001120923.L2_GAC_OC.nc,S2008001134804.L2_GAC_OC.nc,S2008001152646.L2_GAC_OC.nc,S2008001170527.L2_GAC_OC.ncZRQFRHPY](mbFSHDYPx(!wDBTLF:@ & J8%f"\VC 9DB@JFOLU0S[Ya_gSfllr1sxy~'4ÓX:P h< ")07?%4F*^M0T6[;bA@jGqLxR%X}]ڎc=intz QDj,&.n6L>0FNV]emu ~,G!h&,17"<^BGM4RW]:bh mqsx\#~+]4<sENV=_gp7yb&kJ.    !" :+ X4$ |=) F. 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p"O: -O6 8O1 DO, =OO' dZO# eO pO {O ӆO O O O O O O O O O O {O V P ,P P *P 5P V@P KP UP `P 2kP uP P $P P WP P wP P P Pz }Pu Pp bPk Pf 3 Qa Q\ QW G(QR 2QM [Q9 eQ4 pQ/ KzQ* uQ% Q Q բQ Q Q Q Q Q Q Q Q Q R R R l$R @.R 8R AR KR ^UR _R hR rR *|R ЅR qR R R 5R R HR R} GRx Rr 1Rm Rh Rc iS] SX SS qSN &SI 0SC K9S> BS9 KS3 TS. #^S) LgS$ ppS yS S S ɔS ҝS ֦SկSθSSSS~S]S6S SSTfT$T"T+T?4TUFUNUVU^U|fUZnU3vU~UԅUU^UUҤUU0UֻUwUUU8UUGUUz@UtUn"ViVcV]KVX$VR+VLA3VG:VAAV;IV6:PV0jWV*^V%eVlVsV{VVVVVVVVҲVVV`V/VVVxV0VVV4VWp WWWW$W~+Wxrlga[UOJD>82,'! {vpjd^XRLF@:4.(" ysmga[UOIC=71*$ ysmg`ZTNHB;5/)# {uoic\VPJC=71+$ |uoic\VPJC=70*$ {tnha[UOHB<5/)# FHDB\infile./data4/sdpsoper/vdc/vpu3/workbuf/l2inlist.datofileS2008001.L3b_DAY_CHL.ncoformat2fileuseS2008001.L3b_DAY_CHL.contribsday2008001eday2008001resolve9 rowgroup270flaguseATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,CLDICE,COCCOLITH,LOWLW,CHLWARN,CHLFAIL,NAVWARN,MAXAERITER,ATMWARN,HISOLZEN,NAVFAIL,FILTER,HIGLINTl3bprodchlor_a,chl_ocx prodtypeO pversion2014.0suiteCHLaverage standardnight0verbose0minobs0deflate4 qual_prod qual_max255healpix0FHDB M A]Z!identifier_product_doi_authorityhttp://dx.doi.orgidentifier_product_doi$10.5067/ORBVIEW-2/SEAWIFS_OC.2014.0 keywordsLOceans > Ocean Chemistry > Chlorophyll; Oceans > Ocean Optics > Ocean Colorkeywords_vocabulary<NASA Global Change Master Directory (GCMD) Science KeywordsFHDB MA institution[NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Groupstandard_name_vocabulary6NetCDF Climate and Forecast (CF) Metadata ConventionsMetadata_ConventionsUnidata Dataset Discovery v1.0 Conventions'CF-1.6, Unidata Dataset Discovery v1.0naming_authoritygov.nasa.gsfc.sci.oceandataid3S2008001.L3b_DAY_CHL.nc/L3/S2008001.L3b_DAY_CHL.nclicenseRhttp://science.nasa.gov/earth-science/earth-science-data/data-information-policy/publisher_nameNASA/GSFC/OBPGcreator_emaildata@oceancolor.gsfc.nasa.govpublisher_emaildata@oceancolor.gsfc.nasa.gov creator_url#http://oceandata.sci.gsfc.nasa.govpublisher_url#http://oceandata.sci.gsfc.nasa.govprocessing_level L3 BinnedFHDB M=geospatial_lat_max ?@4 4Rgeospatial_lat_min ?@4 4RSgeospatial_lon_max ?@4 4Qe@geospatial_lon_min ?@4 4+d@geospatial_lat_unitsdegrees_northgeospatial_lon_units degrees_eastgeospatial_lon_resolution ?@4 4UUUUUU?geospatial_lat_resolution ?@4 4UUUUUU?spatialResolution9.2 km data_bins percent_data_bins  [6 8units chlor_a:mg m^-3,chl_ocx:mg m^-3binning_schemeIntegerized Sinusoidal Gridproject0Ocean Biology Processing Group (NASA/GSFC/OBPG) creator_nameNASA/GSFC/OBPGFHDB M叹 product_nameS2008001.L3b_DAY_CHL.nctitleSeaWiFS Level-3 Binned Data instrumentSeaWiFS platform Orbview-2temporal_rangeday start_orbit_number  end_orbit_number  date_created2015-10-01T21:32:45.000Zprocessing_version2014.0history%l2bin par=S2008001.L3b_DAY_CHL.paramtime_coverage_start2007-12-31T18:09:01.000Ztime_coverage_end2008-01-01T17:49:13.000Znorthernmost_latitude  southernmost_latitude  Ueasternmost_longitude  *Cwesternmost_longitude  \Q%Ccdm_data_typepointncmeta/inst/extdata/stars/0000755000176200001440000000000014520356511015266 5ustar liggesusersncmeta/inst/extdata/stars/reduced.nc0000644000176200001440000040375414520356511017240 0ustar liggesusersCDF lonlatZzlevtime CDIC@CBCDCFCHCJCLCNCPCRCTCVCXCZC\C^C`CbCdCfChCjClCnCpCrCtCvCxCzC|C~CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC²®ª¦¢žš–’ŽŠ†‚|tld\TLD<4,$ pP0@?@@@@AA0APApAAAAAAAABB BBB$B,B4B<BDBLBTB\BdBlBtB|BBBBBBBBBBBBBD[c^XZdeffgfbWMLLLLo\MTVZfmmgyz}rd\]ehw|~~z|rst}}{hbevr}xzi\`is^a^Xf{r_^eggdbbfiqr{]V~mdabgotvsmidbfbemu|w^RTnuf`oumez~k{tqjc[Z_gjiigedbacmurzuijiglzxwstlge_[bijighouvurtsqkealov~zg[Ybijfbrqovwsi\]]]yl|xfdjlhilmtzwtwtq~kWU]QNUX~yj]xkynmgfkihjqpi^[YX[bwqe`cirywputt{~}iqosuhvqW]UW`VW\aaem~u~ejshjzeoqd[mnk }x~|ywmry{{s~  _QPQQU]lz~vrtt{~& +G9cRk(;dxwj)&+BiE/k~d>4@)RcS,vrwvm,2?:N~|\r}kh\E<8-<8:l%  ?^.l)e3^qNENpNnwyspbj  heYl~^#*Vwx VXSzUAMg+Nq_  `qUH3YhyC6#jA gU_dbgbh|yt , .BWMRhH /9FHMTV+=YP h - v=|5Q'SXF^= Co|7jw[peg[<wz!  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0ustar liggesusersnetcdf test-1 { dimensions: x = 2 ; y = 3 ; c3 = 2 ; c4 = 2 ; c5 = 3 ; variables: double a(c5, c4, c3, y, x) ; double x(x) ; double y(y) ; double c3(c3) ; double c4(c4) ; double c5(c5) ; } ncmeta/inst/extdata/rasterwise-timeseries.cdl0000644000176200001440000000172214520356511021157 0ustar liggesusersnetcdf timeseries { dimensions: station = 1 ; time = 1 ; variables: int num(station) ; num:long_name = "Station number" ; num:cf_role = "timeseries_id" ; int time(time) ; time:units = "days since 1970-01-01 00:00:00 UTC" ; time:long_name = "time" ; time:calendar = "gregorian" ; float pr(station, time) ; pr:units = "kg m-2 s-1" ; pr:_FillValue = -10.f ; pr:long_name = "Total precipitation flux" ; pr:coordinates = "lat lon alt num" ; pr:standard_name = "precipitation_flux" ; float lat(station) ; lat:units = "degrees_north" ; lat:long_name = "Station latitude" ; lat:standard_name = "latitude" ; float lon(station) ; lon:units = "degrees_east" ; lon:long_name = "Station longitude" ; lon:standard_name = "longitude" ; float alt(station) ; alt:units = "m" ; alt:long_name = "Vertical distance above the surface" ; alt:standard_name = "height" ; // global attributes: :featureType = "timeSeries" ; :Conventions = "CF-1.7" ; } ncmeta/inst/extdata/rasterwise-bad_examples_62-example3.nc0000644000176200001440000006024014520356511023313 0ustar liggesusersCDF timeXY CDI?Climate Data Interface version 1.9.1 (http://mpimet.mpg.de/cdi)history;Thu Oct 04 14:37:21 2018: cdo -C seltimestep,1 /home/esp-shared-a/Observations/EURO4M-APGD/EURO4M-APGD-1971-2008.nc example3.nc Fri Nov 1 13:32:07 2013: ncrename -v PRECIPITATION,pr EURO4M-APGD-1971-2008.nc Fri Nov 1 13:29:54 2013: ncrcat RapdD_al05.etrs.laea_19710100.nc RapdD_al05.etrs.laea_19710200.nc RapdD_al05.etrs.laea_19710300.nc RapdD_al05.etrs.laea_19710400.nc RapdD_al05.etrs.laea_19710500.nc RapdD_al05.etrs.laea_19710600.nc RapdD_al05.etrs.laea_19710700.nc RapdD_al05.etrs.laea_19710800.nc RapdD_al05.etrs.laea_19710900.nc RapdD_al05.etrs.laea_19711000.nc RapdD_al05.etrs.laea_19711100.nc RapdD_al05.etrs.laea_19711200.nc RapdD_al05.etrs.laea_19720100.nc RapdD_al05.etrs.laea_19720200.nc RapdD_al05.etrs.laea_19720300.nc RapdD_al05.etrs.laea_19720400.nc RapdD_al05.etrs.laea_19720500.nc RapdD_al05.etrs.laea_19720600.nc RapdD_al05.etrs.laea_19720700.nc RapdD_al05.etrs.laea_19720800.nc RapdD_al05.etrs.laea_19720900.nc RapdD_al05.etrs.laea_19721000.nc RapdD_al05.etrs.laea_19721100.nc RapdD_al05.etrs.laea_19721200.nc RapdD_al05.etrs.laea_19730100.nc RapdD_al05.etrs.laea_19730200.nc RapdD_al05.etrs.laea_19730300.nc RapdD_al05.etrs.laea_19730400.nc RapdD_al05.etrs.laea_19730500.nc RapdD_al05.etrs.laea_19730600.nc RapdD_al05.etrs.laea_19730700.nc RapdD_al05.etrs.laea_19730800.nc RapdD_al05.etrs.laea_19730900.nc RapdD_al05.etrs.laea_19731000.nc RapdD_al05.etrs.laea_19731100.nc RapdD_al05.etrs.laea_19731200.nc RapdD_al05.etrs.laea_19740100.nc RapdD_al05.etrs.laea_19740200.nc RapdD_al05.etrs.laea_19740300.nc RapdD_al05.etrs.laea_19740400.nc RapdD_al05.etrs.laea_19740500.nc RapdD_al05.etrs.laea_19740600.nc RapdD_al05.etrs.laea_19740700.nc RapdD_al05.etrs.laea_19740800.nc RapdD_al05.etrs.laea_19740900.nc RapdD_al05.etrs.laea_19741000.nc RapdD_al05.etrs.laea_19741100.nc RapdD_al05.etrs.laea_19741200.nc RapdD_al05.etrs.laea_19750100.nc RapdD_al05.etrs.laea_19750200.nc RapdD_al05.etrs.laea_19750300.nc RapdD_al05.etrs.laea_19750400.nc RapdD_al05.etrs.laea_19750500.nc RapdD_al05.etrs.laea_19750600.nc RapdD_al05.etrs.laea_19750700.nc RapdD_al05.etrs.laea_19750800.nc RapdD_al05.etrs.laea_19750900.nc RapdD_al05.etrs.laea_19751000.nc RapdD_al05.etrs.laea_19751100.nc RapdD_al05.etrs.laea_19751200.nc RapdD_al05.etrs.laea_19760100.nc RapdD_al05.etrs.laea_19760200.nc RapdD_al05.etrs.laea_19760300.nc RapdD_al05.etrs.laea_19760400.nc RapdD_al05.etrs.laea_19760500.nc RapdD_al05.etrs.laea_19760600.nc RapdD_al05.etrs.laea_19760700.nc RapdD_al05.etrs.laea_19760800.nc RapdD_al05.etrs.laea_19760900.nc 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RapdD_al05.etrs.laea_19961000.nc RapdD_al05.etrs.laea_19961100.nc RapdD_al05.etrs.laea_19961200.nc RapdD_al05.etrs.laea_19970100.nc RapdD_al05.etrs.laea_19970200.nc RapdD_al05.etrs.laea_19970300.nc RapdD_al05.etrs.laea_19970400.nc RapdD_al05.etrs.laea_19970500.nc RapdD_al05.etrs.laea_19970600.nc RapdD_al05.etrs.laea_19970700.nc RapdD_al05.etrs.laea_19970800.nc RapdD_al05.etrs.laea_19970900.nc RapdD_al05.etrs.laea_19971000.nc RapdD_al05.etrs.laea_19971100.nc RapdD_al05.etrs.laea_19971200.nc RapdD_al05.etrs.laea_19980100.nc RapdD_al05.etrs.laea_19980200.nc RapdD_al05.etrs.laea_19980300.nc RapdD_al05.etrs.laea_19980400.nc RapdD_al05.etrs.laea_19980500.nc RapdD_al05.etrs.laea_19980600.nc RapdD_al05.etrs.laea_19980700.nc RapdD_al05.etrs.laea_19980800.nc RapdD_al05.etrs.laea_19980900.nc RapdD_al05.etrs.laea_19981000.nc RapdD_al05.etrs.laea_19981100.nc RapdD_al05.etrs.laea_19981200.nc RapdD_al05.etrs.laea_19990100.nc RapdD_al05.etrs.laea_19990200.nc RapdD_al05.etrs.laea_19990300.nc RapdD_al05.etrs.laea_19990400.nc RapdD_al05.etrs.laea_19990500.nc RapdD_al05.etrs.laea_19990600.nc RapdD_al05.etrs.laea_19990700.nc RapdD_al05.etrs.laea_19990800.nc RapdD_al05.etrs.laea_19990900.nc RapdD_al05.etrs.laea_19991000.nc RapdD_al05.etrs.laea_19991100.nc RapdD_al05.etrs.laea_19991200.nc RapdD_al05.etrs.laea_20000100.nc RapdD_al05.etrs.laea_20000200.nc RapdD_al05.etrs.laea_20000300.nc RapdD_al05.etrs.laea_20000400.nc RapdD_al05.etrs.laea_20000500.nc RapdD_al05.etrs.laea_20000600.nc RapdD_al05.etrs.laea_20000700.nc RapdD_al05.etrs.laea_20000800.nc RapdD_al05.etrs.laea_20000900.nc RapdD_al05.etrs.laea_20001000.nc RapdD_al05.etrs.laea_20001100.nc RapdD_al05.etrs.laea_20001200.nc RapdD_al05.etrs.laea_20010100.nc RapdD_al05.etrs.laea_20010200.nc RapdD_al05.etrs.laea_20010300.nc RapdD_al05.etrs.laea_20010400.nc RapdD_al05.etrs.laea_20010500.nc RapdD_al05.etrs.laea_20010600.nc RapdD_al05.etrs.laea_20010700.nc RapdD_al05.etrs.laea_20010800.nc RapdD_al05.etrs.laea_20010900.nc 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RapdD_al05.etrs.laea_20061000.nc RapdD_al05.etrs.laea_20061100.nc RapdD_al05.etrs.laea_20061200.nc RapdD_al05.etrs.laea_20070100.nc RapdD_al05.etrs.laea_20070200.nc RapdD_al05.etrs.laea_20070300.nc RapdD_al05.etrs.laea_20070400.nc RapdD_al05.etrs.laea_20070500.nc RapdD_al05.etrs.laea_20070600.nc RapdD_al05.etrs.laea_20070700.nc RapdD_al05.etrs.laea_20070800.nc RapdD_al05.etrs.laea_20070900.nc RapdD_al05.etrs.laea_20071000.nc RapdD_al05.etrs.laea_20071100.nc RapdD_al05.etrs.laea_20071200.nc RapdD_al05.etrs.laea_20080100.nc RapdD_al05.etrs.laea_20080200.nc RapdD_al05.etrs.laea_20080300.nc RapdD_al05.etrs.laea_20080400.nc RapdD_al05.etrs.laea_20080500.nc RapdD_al05.etrs.laea_20080600.nc RapdD_al05.etrs.laea_20080700.nc RapdD_al05.etrs.laea_20080800.nc RapdD_al05.etrs.laea_20080900.nc RapdD_al05.etrs.laea_20081000.nc RapdD_al05.etrs.laea_20081100.nc RapdD_al05.etrs.laea_20081200.nc EURO4M-APGD-1971-2008.nc institution8Federal Office of Meteorology and Climatology MeteoSwiss ConventionsCF-1.4 ReferencesIsotta, F.A. et al. 2013: The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data. Int. J. Climatol., accepted. Please check for updates on the publication status!nco_openmp_thread_numberCDO?Climate Data Operators version 1.9.1 (http://mpimet.mpg.de/cdo) time  standard_nametime long_nametimeunitsdays since 1900-01-01 00:00:00calendarstandardaxisTE$lon  standard_name longitude long_namelongitude coordinateunits degrees_east_CoordinateAxisTypeLonElat  standard_namelatitude long_namelatitude coordinateunits degrees_north_CoordinateAxisTypeLatE X  standard_nameprojection_x_coordinate long_namex coordinate of projectionunitsmetersaxisXEY  standard_nameprojection_y_coordinate long_namey coordinate of projectionunitsmetersaxisYE ETRS89-LAEA  missing_valuegrid_mapping_namelambert_azimuthal_equal_arealongitude_of_projection_origin@$latitude_of_projection_origin@J false_eastingAP{false_northingAH}inverse_flattening@rsemi_major_axisAXT@E pr long_name PRECIPITATIONunitsmm.d-1 grid_mapping ETRS89-LAEA coordinateslat lon _FillValuey\ missing_valuey\ grid_nameal05.etrs.laeaversionv1.2 prod_date 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RapdD_al05.etrs.laea_20060600.nc RapdD_al05.etrs.laea_20060700.nc RapdD_al05.etrs.laea_20060800.nc RapdD_al05.etrs.laea_20060900.nc RapdD_al05.etrs.laea_20061000.nc RapdD_al05.etrs.laea_20061100.nc RapdD_al05.etrs.laea_20061200.nc RapdD_al05.etrs.laea_20070100.nc RapdD_al05.etrs.laea_20070200.nc RapdD_al05.etrs.laea_20070300.nc RapdD_al05.etrs.laea_20070400.nc RapdD_al05.etrs.laea_20070500.nc RapdD_al05.etrs.laea_20070600.nc RapdD_al05.etrs.laea_20070700.nc RapdD_al05.etrs.laea_20070800.nc RapdD_al05.etrs.laea_20070900.nc RapdD_al05.etrs.laea_20071000.nc RapdD_al05.etrs.laea_20071100.nc RapdD_al05.etrs.laea_20071200.nc RapdD_al05.etrs.laea_20080100.nc RapdD_al05.etrs.laea_20080200.nc RapdD_al05.etrs.laea_20080300.nc RapdD_al05.etrs.laea_20080400.nc RapdD_al05.etrs.laea_20080500.nc RapdD_al05.etrs.laea_20080600.nc RapdD_al05.etrs.laea_20080700.nc RapdD_al05.etrs.laea_20080800.nc RapdD_al05.etrs.laea_20080900.nc RapdD_al05.etrs.laea_20081000.nc RapdD_al05.etrs.laea_20081100.nc RapdD_al05.etrs.laea_20081200.nc EURO4M-APGD-1971-2008.nc institution8Federal Office of Meteorology and Climatology MeteoSwiss ConventionsCF-1.4 ReferencesIsotta, F.A. et al. 2013: The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data. Int. J. Climatol., accepted. Please check for updates on the publication status!nco_openmp_thread_numberCDO?Climate Data Operators version 1.9.1 (http://mpimet.mpg.de/cdo) time  standard_nametime long_nametimeunitsdays since 1900-01-01 00:00:00calendarstandardaxisTE$lon  standard_name longitude long_namelongitude coordinateunits degrees_east_CoordinateAxisTypeLonElatncmeta/inst/extdata/S2008001.L3m_DAY_CHL_chlor_a_9km.nc0000644000176200001440000100345114520356511021501 0ustar liggesusersHDF  )0OHDR, V V V V" %processing_controlACDlatOHDR, V V V V" #input_parameters.F *software_namesmigenWOHDR, V V V V" 0g11\8%ZFSSEi =i6mOHDR- V V V Vpp   * /404!1ʈ OCHKchlor_a -@HBTHDd( PFSSE1qC$FRHP#i A(/BTHDd( A&BTHD  d( A- 4FSHDePx(CCy^BTLFH 7#e K3 ! >> ej?M<!> U*{#^D95[%- o)) -!{\/8 B'ٕ1"&]4B zT9:(_/<^e@N=f 6+x?kBTLF F\(AM(:(b/;R B @ K@ > <2R2KKKeKHH@HH9 CLDG*/BTINR |/tF(9B . ej?M<!> U*{#^D95[%- o)) -!{\/8 B'ٕ1"&]4B zT9:(_/<^e@N=f 6+x?kBTLFM/> :z =$ U)"( 8,n@GKRz}#eK9ú ?%/vLDAb/E{F:i!H3AR2BH.C8k+ KOzLKw;o4F(<~-<BTIN H# ()M(:(b/;R B @ K@ > <2R2KKKeKHH@HH9 CLDG*/BTLF ^OO- ) -!V /" 7# =$ ?%"&8 B'z B( U) U*f 6+ 8, <- (.R |/ c01 I1z B2 K3o4v1536A7C8^D9F:0;F(<nH=>>j?^e@NJBTLFV /"QHK@ 4Kv15R0;T2_eW1 I1nZ36Db @ cHqddz B2S{ <-7{{AQńFh^OjN C\:A7~tz B(Vr c0@S\(cd@HnH=wK= (.0- g|OHDR- V V V V   * U--b0C OHDR- V V V V$pp   + V@@GSuFRHPy y(Z/EBTHD  d(FSHDPx(%%6OBTLFHL ]?Q@ &'@$MB ^2,]x/RE?+ e >^#i U?>|\ cE ÿ'*qBTLF H^#]@?]?'EEMB @  \ + BFSSEyr A-GCOL  9 9 FSSE99\U OCHK/jrgb9nFRHP99 (!BTHDd( a ROCHK +software_version5.04 4sourceS2008001.L3b_DAY_CHL.nc l2_flag_namesATMFAIL,LAND,HILT,HISATZEN,STRAYLIGHT,CLDICE,COCCOLITH,LOWLW,CHLWARN,CHLFAIL,NAVWARN,MAXAERITER,ATMWARN,HISOLZEN,NAVFAIL,FILTER,HIGLINTz MiFHIB>:_FHIBRNJFBGOCHKlon  eightbitcolorpalette̽5@0yBTHD  d( (KBTHDd(95 &q+FSSE4J3FRHP1(6BTHDd(2sqBTHD  d(4i#FSHDPx(maOBTLF& %al&,42 ) L3P!]; 'cEOktK" c'e) fI?l[$ o u3( |z+% '33.( yӵ*{<b)PBTLF 3I?"b '3Ol&') ( ) 3( [$ % * +%P!PJXeFHDB0LifileS2008001.L3b_DAY_CHL.ncofile$S2008001.L3m_DAY_CHL_chlor_a_9km.ncprodchlor_apalfileE/sdps/sdpsoper/Science/OCSSW/V2015.3/data/common/palette/default.pal processing2014.0meas1stype2datamin 0.010000datamax 20.000000lonwest -180.000000loneast 180.000000 latsouth -90.000000 latnorth 90.000000 resolution9km projectionRECT gap_fill0 seam_lon -180.000000minobs0deflate4oformatnetCDF4 precisionFFHDB+uDIMENSION_LIST FHDBu long_name)Chlorophyll Concentration, OCI Algorithmunitsmg m^-3standard_name:mass_concentration_chlorophyll_concentration_in_sea_water _FillValue   valid_min  o: valid_max  Bdisplay_scalelog display_min ?@4 4{Gz? display_max ?@4 44@ scale_factor  ? add_offset   referenceHu, C., Lee Z., and Franz, B.A. (2012). Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference, J. Geophys. Res., 117, C01011, doi:10.1029/2011JC007395. _Netcdf4Dimid FHDB@qprocessing_level L3 Mappedcdm_data_typegrid!identifier_product_doi_authorityhttp://dx.doi.orgidentifier_product_doihttp://dx.doi.org keywordsLOceans > Ocean Chemistry > Chlorophyll; Oceans > Ocean Optics > Ocean Colorkeywords_vocabulary<NASA Global Change Master Directory (GCMD) Science KeywordsFHDB lC_lastModified2015-10-01T21:33:10.000Z institution[NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Groupstandard_name_vocabulary5NetCDF Climate and Forecast (CF) Metadata ConventionMetadata_ConventionsUnidata Dataset Discovery v1.0naming_authoritygov.nasa.gsfc.sci.oceandataid3S2008001.L3b_DAY_CHL.nc/L3/S2008001.L3b_DAY_CHL.nclicenseRhttp://science.nasa.gov/earth-science/earth-science-data/data-information-policy/ creator_nameNASA/GSFC/OBPGpublisher_nameNASA/GSFC/OBPGcreator_emaildata@oceancolor.gsfc.nasa.govpublisher_emaildata@oceancolor.gsfc.nasa.gov 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2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2TREE9 yC,@ ,@@ ,@ 9 ,@ e ,@ ,@@ ,@ ,@!,@y+,@@+,@+,@+,@),,@@U,,@,,@53,,,@,,-,1-,]-,@-,-,-, .,@9.,e.,.,.,@.,/,A/,/,@/,/,0,I0,@u0,0,0,0,@%1,Q1,y;,;,@;,;,)<,U<,@<,<,<, =,@ 1=, ]=, =, =,@ ,@ x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2OCHK l0REFERENCE_LIST6datasetdimension ̽Wuetcdf4Dimid vFRHP4 (d=BTHD  d(97 4FSHDPx(0BTLF(L Y?Q@$+<A__+e >(il?>|͈EYBTLF +A_+((@?Y?lLDFSHDPx(,:LROHDR- V V V Vy!    +CLASSDIMENSION_SCALE ZNAME@This is a netCDF dimension but not a netCDF variable. 3 l0REFERENCE_LIST6datasetdimension ̽x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2TREE9A!U,@ =, >, 9>, e>,@ >, >, >, ?,@ A?, m?, ?, ?,@ ?, @, I@,u@,@@,@,@,%A,@QA,}A,A,A,@B,-B,YB,B,@B,B, C,5C,@M,M, N,5N,@aN,N,N,N,@O,=O,iO,O,@O,O,P,EP,@qP,P,P,P,@!Q,MQ,yQ,Q,@Q,,@ x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2TREE9yCg,Q,)R,UR,@R,R,R, S,@ 1S, ]S, S, S,@ S, T, 9T, eT,@ T, T, T, U,@ AU, mU, _, `,@ A`, m`, `,`,@`,a,Ia,ua,@a,a,a8%b9@]b,b,b,b,@c,Fc,rc,c,@c,c,"d,Nd,@zd,d,d,d,@*e,Ve,e,e,@e,f,,@ x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 tcg` ѯq fc=U-Tx^1 9CV)X{𴞳U;60x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2TREE9U{,2f,^f,@f,f,f,g,@:g,fg,g,!r,@Mr,yr,r,r,@r,)s,Us, s,@ s, s, t, 1t,@ ]t, t, t, t,@ u, 9u, eu, u,@ u, u, v, Av,@ mv, v, v,v,@w,Iw,uw,w,@w,w,%x,Qx,@}x,x,x,@y,@@-y,@Yy,@y,@y,@@y,@ z,@5z,@,@ x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2TREE<g,@az,@@z,@z,@z,@{,@@={,@i{,@{,@,@@%,@Q,@},@,@@Ն,@,@-,@Y,@@,@,@݇,@ ,@@5,@a,@,@,@@,@,@=,@ i,@@ ,@ ,@ ,@ ,@@ E,@ q,@ ,@ Ɋ,@@ ,@ !,@ M,@ y,@@ ,@ ы,@ ,@ ),@@ U,@ ,@ ,@ٌ,@@,@1,@],@,@@,@,@ ,@9,@@e,@,@x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2x^1 , 6iUUUUUUUUUUUUUUU 2FHDB/4g5CLASSDIMENSION_SCALENAMElat _Netcdf4Dimid  long_name Latitudeunits degree_north _FillValue   valid_min   valid_max  B0REFERENCE_LIST6datasetdimension BTLF)L Y?Q@$+<A__+e >'il?>|̈́uBTLF +A_+)'@?Y?lCBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBVBjB@BVBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBBBUBjB@BUBUBB*BU~B~B*~BU}B}B*}BU|B|B*|BU{B{B*{BUzBzB*zBUyByB*yBUxBxB*xBUwBwB*wBUvBvB*vBUuBuB*uBUtBtB*tBUsBsB*sBUrBrB*rBUqBqB*qBUpBpB*pBUoBoB*oBUnBnB*nBUmBmB*mBUlBlB*lBUkBkB*kBUjBjB*jBUiBiB*iBUhBhB*hBUgBgB*gBUfBfB*fBUeBeB*eBUdBdB*dBUcBcB*cBUbBbB*bBUaBaB*aBU`B`B*`BU_B_B*_BU^B^B*^BU]B]B*]BU\B\B*\BU[B[B*[BUZBZB*ZBUYBYB*YBUXBXB*XBUWBWB*WBUVBVB*VBUUBUB*UBUTBTB*TBUSBSB*SBURBRB*RBUQBQB*QBUPBPB*PBUOBOB*OBUNBNB*NBUMBMB*MBULBLB*LBUKBKB*KBUJBJB*JBUIBIB*IBUHBHB*HBUGBGB*GBUFBFB*FBUEBEB*EBUDBDB*DBUCBCB*CBUBBBB*BBUABAB*ABU@B@B*@BU?B?B*?BU>B>B*>BU=B=B*=BU>T>UUU>=U*=*VUUUUU꾫 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4C0REFERENCE_LIST6datasetdimension OHDR- V V V V!  y3 +CLASSDIMENSION_SCALE ZNAME@This is a netCDF dimension but not a netCDF variable. 2567OHDR- V V V V$ *) \DIMENSION_LIST  r+cU33ê3U33ê3Uu3`3êJ3U53 3ê 3U22ê2U22ê2Uu2`2êJ2U52 2ê 2U11ê1U11ê1Uu1`1êJ1U51 1ê 1U00ê0U00ê0Uu0`0êJ0U50 0ê 0U//ê/U//ê/Uu/`/êJ/U5/ /ê /U..ê.U..ê.Uu.`.êJ.U5. .ê .U--ê-U--ê-Uu-`-êJ-U5- -ê -U,,ê,U,,ê,Uu,`,êJ,U5, ,ê ,U++ê+U++ê+Uu+`+êJ+U5+ +ê +U**ê*U**ê*Uu*`*êJ*U5* *ê *U))ê)U))ê)Uu)`)êJ)U5) )ê )U((ê(U((ê(Uu(`(êJ(U5( (ê (U''ê'U''ê'Uu'`'êJ'U5' 'ê 'U&&ê&U&&ê&Uu&`&êJ&U5& &ê &U%%ê%U%%ê%Uu%`%êJ%U5% %ê %U$$ê$U$$ê$Uu$`$êJ$U5$ $ê $U##ê#U##ê#Uu#`#êJ#U5# #ê #U""ê"U""ê"Uu"`"êJ"U5" "ê "U!!ê!U!!ê!Uu!`!êJ!U5! !ê !U ê U ê Uu ` êJ U5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu`êJU5 ê UêUêUu_êJU5ê UêUêUu_êJU5ê UêUêUu_êJU5ê UêUêUu_êJU5ê UêUêUu_êJU5ê UêUêUu_êJU5ê U ê U ê Uu _ êJ U5  ê U ê U ê Uu _ êJ U5  ê U ê U ê Uu _ êJ 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'CW5'CJ'C`'CWu'C'C'CW'C'C'CW'C (C (CW5(CJ(C`(CWu(C(C(CW(C(C(CW(C )C )CW5)CJ)C`)CWu)C)C)CW)C)C)CW)C *C *CW5*CJ*C`*CWu*C*C*CW*C*C*CW*C +C +CW5+CJ+C`+CWu+C+C+CW+C+C+CW+C ,C ,CW5,CJ,C`,CWu,C,C,CW,C,C,CW,C -C -CW5-CJ-C`-CWu-C-C-CW-C-C-CW-C .C .CW5.CJ.C`.CWu.C.C.CW.C.C.CW.C /C /CW5/CJ/C`/CWu/C/C/CW/C/C/CW/C 0C 0CW50CJ0C`0CWu0C0C0CW0C0C0CW0C 1C 1CW51CJ1C`1CWu1C1C1CW1C1C1CW1C 2C 2CW52CJ2C`2CWu2C2C2CW2C2C2CW2C 3C 3CW53CJ3C`3CWu3C3C3CW3C3C3CW3Clorux{~~{xurolifc`]ZWTQNKHEB?<9630-*'$!   %*05:@EJPUZ`ejpuzwog_WOG?7/' (08@HPX`hpx{wsokgc_[WSOKGC?;73/+'# }xsnincmeta/inst/extdata/gridmet_sample.cdl0000644000176200001440000000737214520356511017623 0ustar liggesusersnetcdf pr_2019 { dimensions: lon = 1 ; lat = 1 ; day = 1 ; crs = 1 ; variables: double lon(lon) ; lon:units = "degrees_east" ; lon:description = "longitude" ; lon:long_name = "longitude" ; lon:standard_name = "longitude" ; lon:axis = "X" ; double lat(lat) ; lat:units = "degrees_north" ; lat:description = "latitude" ; lat:long_name = "latitude" ; lat:standard_name = "latitude" ; lat:axis = "Y" ; double day(day) ; day:description = "days since 1900-01-01" ; day:units = "days since 1900-01-01 00:00:00" ; day:long_name = "time" ; day:standard_name = "time" ; day:calendar = "gregorian" ; ushort crs(crs) ; crs:grid_mapping_name = "latitude_longitude" ; crs:longitude_of_prime_meridian = 0. ; crs:semi_major_axis = 6378137. ; crs:long_name = "WGS 84" ; crs:inverse_flattening = 298.257223563 ; crs:GeoTransform = "-124.7666666333333 0.041666666666666 0 49.400000000000000 -0.041666666666666" ; crs:spatial_ref = "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AUTHORITY[\"EPSG\",\"4326\"]]" ; crs:_Storage = "chunked" ; crs:_ChunkSizes = 1 ; crs:_DeflateLevel = 9 ; crs:_Endianness = "little" ; ushort precipitation_amount(day, lat, lon) ; precipitation_amount:_FillValue = 32767US ; precipitation_amount:units = "mm" ; precipitation_amount:description = "Daily Accumulated Precipitation" ; precipitation_amount:long_name = "pr" ; precipitation_amount:standard_name = "pr" ; precipitation_amount:missing_value = 32767s ; precipitation_amount:dimensions = "lon lat time" ; precipitation_amount:grid_mapping = "crs" ; precipitation_amount:coordinate_system = "WGS84,EPSG:4326" ; precipitation_amount:scale_factor = 0.1 ; precipitation_amount:add_offset = 0. ; precipitation_amount:coordinates = "lon lat" ; precipitation_amount:_Unsigned = "true" ; // global attributes: :geospatial_bounds_crs = "EPSG:4326" ; :Conventions = "CF-1.6" ; :geospatial_bounds = "POLYGON((-124.7666666333333 49.400000000000000, -124.7666666333333 25.066666666666666, -67.058333300000015 25.066666666666666, -67.058333300000015 49.400000000000000, -124.7666666333333 49.400000000000000))" ; :geospatial_lat_min = "25.066666666666666" ; :geospatial_lat_max = "49.40000000000000" ; :geospatial_lon_min = "-124.7666666333333" ; :geospatial_lon_max = "-67.058333300000015" ; :geospatial_lon_resolution = "0.041666666666666" ; :geospatial_lat_resolution = "0.041666666666666" ; :geospatial_lat_units = "decimal_degrees north" ; :geospatial_lon_units = "decimal_degrees east" ; :coordinate_system = "EPSG:4326" ; :author = "John Abatzoglou - University of Idaho, jabatzoglou@uidaho.edu" ; :date = "01 August 2019" ; :note1 = "The projection information for this file is: GCS WGS 1984." ; :note2 = "Citation: Abatzoglou, J.T., 2013, Development of gridded surface meteorological data for ecological applications and modeling, International Journal of Climatology, DOI: 10.1002/joc.3413" ; :last_permanent_slice = "152" ; :last_early_slice = "212" ; :last_provisional_slice = "206" ; :note3 = "Data in slices after last_permanent_slice (1-based) are considered provisional and subject to change with subsequent updates" ; :note4 = "Data in slices after last_provisional_slice (1-based) are considered early and subject to change with subsequent updates" ; :note5 = "Days correspond approximately to calendar days ending at midnight, Mountain Standard Time (7 UTC the next calendar day)" ; :_NCProperties = "version=2,netcdf=4.6.3,hdf5=1.10.5" ; :_SuperblockVersion = 2 ; :_IsNetcdf4 = 1 ; :_Format = "netCDF-4" ; } ncmeta/inst/extdata/guam.nc0000644000176200001440000073064014520356511015417 0ustar liggesusersCDF Time south_northD west_east> historyDataset was processed from WRF output format into NetCDF-CF conformant format by pnorton@usgs.gov and dblodgett@usgs.gov in September 2016. _NCProperties6version=1|netcdflibversion=4.4.1|hdf5libversion=1.8.16nco_openmp_thread_number ConventionsCF-1.6acknowledgmentYhttps://nccwsc.usgs.gov/display-project/4f8c650ae4b0546c0c397b48/52165f17e4b0b45d6ba394fbMetadata_ConventionsUnidata Dataset Discovery v1.0summary-- Methods -- The Weather Research and Forecast model (WRF) is applied to construct triply-nested meshes. The outermost domain (20-km horizontal resolution) is large enough to cover almost the whole tropical and subtropical areas from the central Pacific to the western Pacific. The intermediate domain has 4-km horizontal resolution, and the innermost domain of each individual island has a horizontal resolution of 0.8 km. The driving fields for the atmosphere are the NASA Modern-Era Retrospective Analysis for Research and Applications reanalysis, and the sea surface temperature (SST) is from NOAA. Variables in the driving fields include temperature, wind, geopotential height, water vapor, etc. For the future runs, the global warming signals are added to the present-day driving fields. This method is called the Pseudo-Global-Warming method which is currently used in various applications. The global warming signals are from 12 CIMP5 models. -- Datasets -- There are three runs. The present-day run starts on January 1st 1990 and ends on December 31st 2009. The purpose of the present-day run is twofold, e.g., to retrieve the present-day climate and to provide the downscaled high-resolution climate data. The future runs include two scenarios, one for RCP4.5 and the other one for RCP8.5. The future runs represent the time period from January 1st 2080 to December 31st 2099 although the date stamps used for projections are the same as those for the present-day run. The dynamical downscaling provides hourly atmospheric and land surface variables, such as rainfall, surface sensible heat fluxes and evaporation, radiative fluxes, wind, and temperature.titleIDynamical Downscaled and Projected Climate for the Pacific Islands - Guamidcida.usgs.gov/samoanaming_authority cida.usgs.gov cdm_data_typeGridpublisher_nameDavid Blodgett publisher_urlhttps://owi.usgs.gov/ creator_name Chunxi Zhang creator_emailchunxi@hawaii.edutime_coverage_start1990-01-01T00:00time_coverage_end2009-12-31T00:00 date_modified 2016-10-01 date_issued 2016-10-01projectVery Fine Resolution Dynamical Downscaling of Past and Future Climates for Assessment of Climate Change Impacts on the Islands of O`ahu and Kaua`i https://nccwsc.usgs.gov/display-project/4f8c650ae4b0546c0c397b48/52165f17e4b0b45d6ba394fbpublisher_emaildblodgett@usgs.govgeospatial_lat_min@*l9geospatial_lat_max@+\Mgeospatial_lon_min@b)geospatial_lon_max@b 5licenseFreely availableHistoryTranslated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2) Original Dataset = guam; Translation Date = 2018-12-31T02:01:41.514Z RAINNC_present  coordinatesTime XLAT XLONG FieldTypeh MemoryOrderXY description*ACCUMULATED TOTAL GRID SCALE PRECIPITATIONunitsmmstagger _ChunkSizes long_name*ACCUMULATED TOTAL GRID SCALE PRECIPITATIONATime FieldTypeh MemoryOrder0 description!minutes since 1989-12-01 00:00:00units!minutes since 1989-12-01 00:00:00staggercalendarstandard long_name!minutes since 1989-12-01 00:00:00bounds time_bnds _ChunkSizes_CoordinateAxisTypeTimeXLAT FieldTypeh MemoryOrderXY descriptionLATITUDE, SOUTH IS NEGATIVEunits degrees_northstagger standard_namelatitude cell_methods Time: mean _ChunkSizesD> long_nameLATITUDE, SOUTH IS NEGATIVE_CoordinateAxisTypeLatATXLONG FieldTypeh MemoryOrderXY descriptionLONGITUDE, WEST IS NEGATIVEunits degrees_eaststagger standard_name longitude cell_methods Time: mean _ChunkSizesD> long_nameLONGITUDE, WEST IS NEGATIVE_CoordinateAxisTypeLonAY4 T2_present  coordinatesTime XLAT XLONG FieldTypeh MemoryOrderXY description TEMP at 2 MunitsKstagger _ChunkSizes long_name TEMP at 2 MA U10_present  coordinatesTime XLAT XLONG FieldTypeh MemoryOrderXY description U at 10 Munitsm s-1stagger _ChunkSizes long_name U at 10 MA V10_present  coordinatesTime XLAT XLONG FieldTypeh MemoryOrderXY description V at 10 Munitsm s-1stagger _ChunkSizes long_name V at 10 MA`ASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaASaAS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS~AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0AS0ASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐASԐAS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AS@AT 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coordinate" ; lon:units = "degrees_east" ; lon:_CoordinateAxisType = "Lon" ; float lat(Y, X) ; lat:standard_name = "latitude" ; lat:long_name = "latitude coordinate" ; lat:units = "degrees_north" ; lat:_CoordinateAxisType = "Lat" ; double X(X) ; X:standard_name = "projection_x_coordinate" ; X:long_name = "x coordinate of projection" ; X:units = "meters" ; X:axis = "X" ; double Y(Y) ; Y:standard_name = "projection_y_coordinate" ; Y:long_name = "y coordinate of projection" ; Y:units = "meters" ; Y:axis = "Y" ; int ETRS89-LAEA ; ETRS89-LAEA:missing_value = -1. ; ETRS89-LAEA:grid_mapping_name = "lambert_azimuthal_equal_area" ; ETRS89-LAEA:longitude_of_projection_origin = 10. ; ETRS89-LAEA:latitude_of_projection_origin = 52. ; ETRS89-LAEA:false_easting = 4321000. ; ETRS89-LAEA:false_northing = 3210000. ; ETRS89-LAEA:inverse_flattening = 298.257222101 ; ETRS89-LAEA:semi_major_axis = 6378137. ; float pr(time, Y, X) ; pr:long_name = "PRECIPITATION" ; pr:units = "mm.d-1" ; pr:grid_mapping = "ETRS89-LAEA" ; pr:coordinates = "lat lon" ; pr:_FillValue = -999.99f ; pr:missing_value = -999.99f ; pr:grid_name = "al05.etrs.laea" ; pr:version = "v1.2" ; pr:prod_date = "2013-07-15" ; // global attributes: :CDI = "Climate Data Interface version 1.9.1 (http://mpimet.mpg.de/cdi)" ; :history = "Thu Oct 04 14:37:21 2018: cdo -C seltimestep,1 /home/esp-shared-a/Observations/EURO4M-APGD/EURO4M-APGD-1971-2008.nc example3.nc\n", "Fri Nov 1 13:32:07 2013: ncrename -v PRECIPITATION,pr EURO4M-APGD-1971-2008.nc\n", "Fri Nov 1 13:29:54 2013: ncrcat RapdD_al05.etrs.laea_19710100.nc RapdD_al05.etrs.laea_19710200.nc RapdD_al05.etrs.laea_19710300.nc RapdD_al05.etrs.laea_19710400.nc RapdD_al05.etrs.laea_19710500.nc RapdD_al05.etrs.laea_19710600.nc RapdD_al05.etrs.laea_19710700.nc RapdD_al05.etrs.laea_19710800.nc RapdD_al05.etrs.laea_19710900.nc RapdD_al05.etrs.laea_19711000.nc RapdD_al05.etrs.laea_19711100.nc RapdD_al05.etrs.laea_19711200.nc RapdD_al05.etrs.laea_19720100.nc RapdD_al05.etrs.laea_19720200.nc RapdD_al05.etrs.laea_19720300.nc RapdD_al05.etrs.laea_19720400.nc RapdD_al05.etrs.laea_19720500.nc RapdD_al05.etrs.laea_19720600.nc RapdD_al05.etrs.laea_19720700.nc RapdD_al05.etrs.laea_19720800.nc RapdD_al05.etrs.laea_19720900.nc RapdD_al05.etrs.laea_19721000.nc RapdD_al05.etrs.laea_19721100.nc RapdD_al05.etrs.laea_19721200.nc RapdD_al05.etrs.laea_19730100.nc RapdD_al05.etrs.laea_19730200.nc RapdD_al05.etrs.laea_19730300.nc RapdD_al05.etrs.laea_19730400.nc RapdD_al05.etrs.laea_19730500.nc RapdD_al05.etrs.laea_19730600.nc RapdD_al05.etrs.laea_19730700.nc RapdD_al05.etrs.laea_19730800.nc RapdD_al05.etrs.laea_19730900.nc RapdD_al05.etrs.laea_19731000.nc RapdD_al05.etrs.laea_19731100.nc RapdD_al05.etrs.laea_19731200.nc RapdD_al05.etrs.laea_19740100.nc RapdD_al05.etrs.laea_19740200.nc RapdD_al05.etrs.laea_19740300.nc RapdD_al05.etrs.laea_19740400.nc RapdD_al05.etrs.laea_19740500.nc RapdD_al05.etrs.laea_19740600.nc RapdD_al05.etrs.laea_19740700.nc RapdD_al05.etrs.laea_19740800.nc RapdD_al05.etrs.laea_19740900.nc RapdD_al05.etrs.laea_19741000.nc RapdD_al05.etrs.laea_19741100.nc RapdD_al05.etrs.laea_19741200.nc RapdD_al05.etrs.laea_19750100.nc RapdD_al05.etrs.laea_19750200.nc RapdD_al05.etrs.laea_19750300.nc RapdD_al05.etrs.laea_19750400.nc RapdD_al05.etrs.laea_19750500.nc RapdD_al05.etrs.laea_19750600.nc RapdD_al05.etrs.laea_19750700.nc RapdD_al05.etrs.laea_19750800.nc RapdD_al05.etrs.laea_19750900.nc RapdD_al05.etrs.laea_19751000.nc RapdD_al05.etrs.laea_19751100.nc RapdD_al05.etrs.laea_19751200.nc RapdD_al05.etrs.laea_19760100.nc RapdD_al05.etrs.laea_19760200.nc RapdD_al05.etrs.laea_19760300.nc RapdD_al05.etrs.laea_19760400.nc RapdD_al05.etrs.laea_19760500.nc RapdD_al05.etrs.laea_19760600.nc RapdD_al05.etrs.laea_19760700.nc RapdD_al05.etrs.laea_19760800.nc RapdD_al05.etrs.laea_19760900.nc RapdD_al05.etrs.laea_19761000.nc RapdD_al05.etrs.laea_19761100.nc RapdD_al05.etrs.laea_19761200.nc RapdD_al05.etrs.laea_19770100.nc RapdD_al05.etrs.laea_19770200.nc RapdD_al05.etrs.laea_19770300.nc RapdD_al05.etrs.laea_19770400.nc RapdD_al05.etrs.laea_19770500.nc RapdD_al05.etrs.laea_19770600.nc RapdD_al05.etrs.laea_19770700.nc RapdD_al05.etrs.laea_19770800.nc RapdD_al05.etrs.laea_19770900.nc RapdD_al05.etrs.laea_19771000.nc RapdD_al05.etrs.laea_19771100.nc RapdD_al05.etrs.laea_19771200.nc RapdD_al05.etrs.laea_19780100.nc RapdD_al05.etrs.laea_19780200.nc RapdD_al05.etrs.laea_19780300.nc RapdD_al05.etrs.laea_19780400.nc RapdD_al05.etrs.laea_19780500.nc RapdD_al05.etrs.laea_19780600.nc RapdD_al05.etrs.laea_19780700.nc RapdD_al05.etrs.laea_19780800.nc RapdD_al05.etrs.laea_19780900.nc RapdD_al05.etrs.laea_19781000.nc RapdD_al05.etrs.laea_19781100.nc RapdD_al05.etrs.laea_19781200.nc RapdD_al05.etrs.laea_19790100.nc RapdD_al05.etrs.laea_19790200.nc RapdD_al05.etrs.laea_19790300.nc RapdD_al05.etrs.laea_19790400.nc RapdD_al05.etrs.laea_19790500.nc RapdD_al05.etrs.laea_19790600.nc RapdD_al05.etrs.laea_19790700.nc RapdD_al05.etrs.laea_19790800.nc RapdD_al05.etrs.laea_19790900.nc RapdD_al05.etrs.laea_19791000.nc RapdD_al05.etrs.laea_19791100.nc RapdD_al05.etrs.laea_19791200.nc RapdD_al05.etrs.laea_19800100.nc RapdD_al05.etrs.laea_19800200.nc RapdD_al05.etrs.laea_19800300.nc RapdD_al05.etrs.laea_19800400.nc RapdD_al05.etrs.laea_19800500.nc RapdD_al05.etrs.laea_19800600.nc RapdD_al05.etrs.laea_19800700.nc RapdD_al05.etrs.laea_19800800.nc RapdD_al05.etrs.laea_19800900.nc RapdD_al05.etrs.laea_19801000.nc RapdD_al05.etrs.laea_19801100.nc RapdD_al05.etrs.laea_19801200.nc RapdD_al05.etrs.laea_19810100.nc RapdD_al05.etrs.laea_19810200.nc RapdD_al05.etrs.laea_19810300.nc RapdD_al05.etrs.laea_19810400.nc RapdD_al05.etrs.laea_19810500.nc RapdD_al05.etrs.laea_19810600.nc RapdD_al05.etrs.laea_19810700.nc RapdD_al05.etrs.laea_19810800.nc RapdD_al05.etrs.laea_19810900.nc RapdD_al05.etrs.laea_19811000.nc RapdD_al05.etrs.laea_19811100.nc RapdD_al05.etrs.laea_19811200.nc RapdD_al05.etrs.laea_19820100.nc RapdD_al05.etrs.laea_19820200.nc RapdD_al05.etrs.laea_19820300.nc RapdD_al05.etrs.laea_19820400.nc RapdD_al05.etrs.laea_19820500.nc RapdD_al05.etrs.laea_19820600.nc RapdD_al05.etrs.laea_19820700.nc RapdD_al05.etrs.laea_19820800.nc RapdD_al05.etrs.laea_19820900.nc RapdD_al05.etrs.laea_19821000.nc RapdD_al05.etrs.laea_19821100.nc RapdD_al05.etrs.laea_19821200.nc RapdD_al05.etrs.laea_19830100.nc RapdD_al05.etrs.laea_19830200.nc RapdD_al05.etrs.laea_19830300.nc RapdD_al05.etrs.laea_19830400.nc RapdD_al05.etrs.laea_19830500.nc RapdD_al05.etrs.laea_19830600.nc RapdD_al05.etrs.laea_19830700.nc RapdD_al05.etrs.laea_19830800.nc RapdD_al05.etrs.laea_19830900.nc RapdD_al05.etrs.laea_19831000.nc RapdD_al05.etrs.laea_19831100.nc RapdD_al05.etrs.laea_19831200.nc RapdD_al05.etrs.laea_19840100.nc RapdD_al05.etrs.laea_19840200.nc RapdD_al05.etrs.laea_19840300.nc RapdD_al05.etrs.laea_19840400.nc RapdD_al05.etrs.laea_19840500.nc RapdD_al05.etrs.laea_19840600.nc RapdD_al05.etrs.laea_19840700.nc RapdD_al05.etrs.laea_19840800.nc RapdD_al05.etrs.laea_19840900.nc RapdD_al05.etrs.laea_19841000.nc RapdD_al05.etrs.laea_19841100.nc RapdD_al05.etrs.laea_19841200.nc RapdD_al05.etrs.laea_19850100.nc RapdD_al05.etrs.laea_19850200.nc RapdD_al05.etrs.laea_19850300.nc RapdD_al05.etrs.laea_19850400.nc RapdD_al05.etrs.laea_19850500.nc RapdD_al05.etrs.laea_19850600.nc RapdD_al05.etrs.laea_19850700.nc RapdD_al05.etrs.laea_19850800.nc RapdD_al05.etrs.laea_19850900.nc RapdD_al05.etrs.laea_19851000.nc RapdD_al05.etrs.laea_19851100.nc RapdD_al05.etrs.laea_19851200.nc RapdD_al05.etrs.laea_19860100.nc RapdD_al05.etrs.laea_19860200.nc RapdD_al05.etrs.laea_19860300.nc RapdD_al05.etrs.laea_19860400.nc RapdD_al05.etrs.laea_19860500.nc RapdD_al05.etrs.laea_19860600.nc RapdD_al05.etrs.laea_19860700.nc RapdD_al05.etrs.laea_19860800.nc RapdD_al05.etrs.laea_19860900.nc RapdD_al05.etrs.laea_19861000.nc RapdD_al05.etrs.laea_19861100.nc RapdD_al05.etrs.laea_19861200.nc RapdD_al05.etrs.laea_19870100.nc 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analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data. Int. J. Climatol., accepted. Please check for updates on the publication status!" ; :nco_openmp_thread_number = 1 ; :CDO = "Climate Data Operators version 1.9.1 (http://mpimet.mpg.de/cdo)" ; } ncmeta/inst/extdata/gridmet_sample.nc0000644000176200001440000006004014520356511017450 0ustar liggesusersHDF   ``OHDR 8" 6\lonlat dayN crs'precipitation_amountCcFRHPz (__BTHDd(.BTHD  d(8JFSHDPx(4?//!qBTLF}қ-B;3} n>qE o)Jl3-ZD {\/9 7AqddVk] rIjN@C )mO.0-3_s. Uy3!98{B3AZVIzLO:BTLF 9O.}{BAB@CIIE ZD 9 ] s. Z33;3n J8iOHDR 2 ?@4 4G G :::wƙOHDR 0 ?@4 4G G 4f55@ܠOHDR V ?@4 4G G /60\0$OHDR    deflate  +Q*gFRHPMw (O bBTHDd( ltHBTHD  d( W=FSHDMPx(c?LtIBTLF.L XKX 0<@P`A[F$_j4e >u` 8( ]Bú8{ $BTLF 0F$j4B`@P.Xu( *OHDR 8  t,݀FRHPLR(K1BTHDd(XLBTHD  d(14FSHDLPx(~?n-1BTLF*L P &'4VjB$IP ^2, ? 7, HO?*x/RiBD` "i+ļ/ _}DpcTcGG?BTLF TjB"G*?*iB4+ ? IP P / , DpxYGCOLN N  N  `FRHP0 (GP$BTHDd(0 v8BTHD  d(2 (WFFSHDPx(?IBTLF,L q1 0<E,x/RF$_j4e >>i=GRzBTLF 0F$j4=>,E,q1pFRHP5 (C'y#BTHDd(6 BTHD  d(8 etFSHDPx(?եBTLF0L [!B70<+0x/RF$_j4e >-i|0G:K0BTLF 0F$j4-00+0[!| zFRHP: (?UABTHDd(4; xdBTHD  d(4= 4FSHDPx(?;|BTLF1L ]!B70<,1x/RF$_j4e >,i~1G*BTLF 0F$j4,11,1]!~I.FSSE)SY"FFSSEwM]=FSSERLGFSSE0ZݡFSSE54U;FSSE:6交FHDB:BzCLASSDIMENSION_SCALENAMElon _Netcdf4Dimid units degrees_east description longitude long_name longitudestandard_name longitudeaxisXtREFERENCE_LISTdatasetdimension FHDB4j8JCLASSDIMENSION_SCALENAMElat _Netcdf4Dimid units degrees_north descriptionlatitude long_namelatitudestandard_namelatitudeaxisYtREFERENCE_LISTdatasetdimension FHDB/dCLASSDIMENSION_SCALENAMEday _Netcdf4Dimid  descriptiondays since 1900-01-01unitsdays since 1900-01-01 00:00:00 long_nametimestandard_nametime calendar gregoriantREFERENCE_LISTdatasetdimension FHDBtZ{ _Netcdf4Coordinates  _FillValueunitsmm descriptionDaily Accumulated Precipitation long_nameprstandard_namepr missing_value dimensions lon lat time grid_mappingcrscoordinate_systemWGS84,EPSG:4326 scale_factor ?@4 4? add_offset ?@4 4 coordinateslon lat _UnsignedtrueDIMENSION_LISTFHDB`QCLASSDIMENSION_SCALENAMEcrs _Netcdf4Dimid grid_mapping_namelatitude_longitudelongitude_of_prime_meridian ?@4 4semi_major_axis ?@4 4@TXA long_nameWGS 84inverse_flattening ?@4 4mtr@ GeoTransformM-124.7666666333333 0.041666666666666 0 49.400000000000000 -0.041666666666666 spatial_refGEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]]FHDBdnote5wDays correspond approximately to calendar days ending at midnight, Mountain Standard Time (7 UTC the next calendar day)FHDBiauthor=John Abatzoglou - University of Idaho, jabatzoglou@uidaho.edudate01 August 2019note1:The projection information for this file is: GCS WGS 1984.note2Citation: Abatzoglou, J.T., 2013, Development of gridded surface meteorological data for ecological applications and modeling, International Journal of Climatology, DOI: 10.1002/joc.3413last_permanent_slice152last_early_slice212last_provisional_slice206note3|Data in slices after last_permanent_slice (1-based) are considered provisional and subject to change with subsequent updatesnote4xData in slices after last_provisional_slice (1-based) are considered early and subject to change with subsequent updates_NCProperties"version=2,netcdf=4.6.3,hdf5=1.10.5FHDBgeospatial_bounds_crs EPSG:4326 ConventionsCF-1.6geospatial_boundsPOLYGON((-124.7666666333333 49.400000000000000, -124.7666666333333 25.066666666666666, -67.058333300000015 25.066666666666666, -67.058333300000015 49.400000000000000, -124.7666666333333 49.400000000000000))geospatial_lat_min25.066666666666666geospatial_lat_max49.40000000000000geospatial_lon_min-124.7666666333333geospatial_lon_max-67.058333300000015geospatial_lon_resolution0.041666666666666geospatial_lat_resolution0.041666666666666geospatial_lat_unitsdecimal_degrees northgeospatial_lon_unitsdecimal_degrees eastcoordinate_system EPSG:4326FHIB[WSڐncmeta/inst/extdata/rasterwise-high-dim-test-1.nc0000644000176200001440000000174414520356511021451 0ustar liggesusersCDF xyc3c4c5 a@Dxyc3c4c5GGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGncmeta/inst/extdata/avhrr-only-v2.19810901_header.nc0000644000176200001440000000647414520356511021340 0ustar liggesusersCDF timezlevlat lon  ConventionsCF-1.0title6Daily-OI-V2, final, Data (Ship, Buoy, AVHRR, GSFC-ice)History Version 2.0 creation_date 2011-05-04Source"NOAA/National Climatic Data CenterContact^Dick Reynolds, email: Richard.W.Reynolds@noaa.gov & Chunying Liu, email: Chunying.liu@noaa.gov time  long_nameCenter time of the dayunitsdays since 1978-01-01 00:00:00tzlev  long_nameSea surface heightunitsmeters actual_range0, 0xlat  long_nameLatitudeunits degrees_northgrids+Uniform grid from -89.875 to 89.875 by 0.25(|lon  long_name Longitudeunits degrees_eastgrids*Uniform grid from 0.125 to 359.875 by 0.258sst  long_nameDaily sea surface temperatureunits degrees C _FillValue add_offset scale_factor<# valid_min valid_maxanom  long_name'Daily sea surface temperature anomaliesunits degrees C _FillValue add_offset scale_factor<# valid_minP valid_max err  long_name2Estimated error standard deviation of analysed_sstunits degrees C _FillValue add_offset scale_factor<# valid_min valid_max ice  long_nameSea ice concentrationunits percentage _FillValue add_offset scale_factor<# valid_min valid_maxd $||||||||||||||||||||||||||