Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
warning = FALSE
)
data.table::setDTthreads(2)
## ----setup--------------------------------------------------------------------
library(OpenSpecy)
## ---- eval=FALSE--------------------------------------------------------------
# run_app()
## ---- eval=FALSE--------------------------------------------------------------
# read_any("path/to/your/data")
## ---- eval=FALSE--------------------------------------------------------------
# read_text(".csv")
# read_asp(".asp")
# read_opus(".0")
## -----------------------------------------------------------------------------
data("raman_hdpe")
## -----------------------------------------------------------------------------
spectral_map <- read_extdata("CA_tiny_map.zip") |>
read_any() # preserves some metadata
asp_example <- read_extdata("ftir_ldpe_soil.asp") |>
read_any()
ps_example <- read_extdata("ftir_ps.0") |>
read_any() # preserves some metadata
csv_example <- read_extdata("raman_hdpe.csv") |>
read_any()
json_example <- read_extdata("raman_hdpe.json") |>
read_any() # read in exactly as an OpenSpecy object
## -----------------------------------------------------------------------------
scratch_OpenSpecy <- as_OpenSpecy(x = seq(1000,2000, by = 5),
spectra = data.frame(runif(n = 201)),
metadata = list(file_name = "fake_noise"))
## -----------------------------------------------------------------------------
# Access the wavenumbers
scratch_OpenSpecy$wavenumber
## -----------------------------------------------------------------------------
# Access the spectra
scratch_OpenSpecy$spectra
## -----------------------------------------------------------------------------
# Access the metadata
scratch_OpenSpecy$metadata
## -----------------------------------------------------------------------------
# Performs checks to ensure that OpenSpecy objects are adhering to our standards;
# returns TRUE if it passes.
check_OpenSpecy(scratch_OpenSpecy)
# Checks only the object type to make sure it has OpenSpecy type
is_OpenSpecy(scratch_OpenSpecy)
## -----------------------------------------------------------------------------
print(scratch_OpenSpecy) # shows the raw object
## -----------------------------------------------------------------------------
summary(scratch_OpenSpecy) # summarizes the contents of the spectra
## -----------------------------------------------------------------------------
head(scratch_OpenSpecy) # shows the top wavenumbers and intensities
## ---- eval=F------------------------------------------------------------------
# write_spec(scratch_OpenSpecy, "test_scratch_OpenSpecy.yml", digits = 5)
# write_spec(scratch_OpenSpecy, "test_scratch_OpenSpecy.json", digits = 5)
## ---- eval=F------------------------------------------------------------------
# hyperspecy <- as_hyperSpec(scratch_OpenSpecy)
## ---- fig.align="center", fig.width=5-----------------------------------------
plot(scratch_OpenSpecy) # quick and efficient
## ---- fig.align="center", out.width="100%"------------------------------------
# This will min-max normalize your data even if it isn't already but are not
# changing your underlying data
plotly_spec(scratch_OpenSpecy, json_example)
## ---- eval=F------------------------------------------------------------------
# heatmap_spec(spectral_map,
# z = spectral_map$metadata$x)
## ---- fig.align="center", out.width="100%"------------------------------------
interactive_plot(spectral_map, select = 100, z = spectral_map$metadata$x)
## ---- fig.align="center", fig.width=5-----------------------------------------
c_spec(list(asp_example, ps_example), range = "common", res = 5) |>
plot()
## -----------------------------------------------------------------------------
# Extract the 150th spectrum.
filter_spec(spectral_map, 150)
## -----------------------------------------------------------------------------
# Extract the spectrum with column name "8_5".
filter_spec(spectral_map, "8_5") |>
print()
## ---- fig.align="center", fig.width=5-----------------------------------------
# Extract the spectra with a logical argument based on metadata
filter_spec(spectral_map, spectral_map$metadata$y == 1) |>
plot()
## ---- fig.align="center", fig.width=5-----------------------------------------
sample_spec(spectral_map, size = 5) |>
plot()
## -----------------------------------------------------------------------------
processed <- process_spec(raman_hdpe,
active = TRUE,
adj_intens = FALSE,
adj_intens_args = list(type = "none"),
conform_spec = TRUE,
conform_spec_args = list(range = NULL, res = 5,
type = "interp"),
restrict_range = FALSE,
restrict_range_args = list(min = 0, max = 6000),
flatten_range = FALSE,
flatten_range_args = list(min = 2200, max = 2420),
subtr_baseline = FALSE,
subtr_baseline_args = list(type = "polynomial",
degree = 8, raw = FALSE,
baseline = NULL),
smooth_intens = TRUE,
smooth_intens_args = list(polynomial = 3, window = 11,
derivative = 1, abs = TRUE),
make_rel = TRUE)
summary(processed)
summary(raman_hdpe)
## ----eval=FALSE---------------------------------------------------------------
# plotly_spec(raman_hdpe, processed)
## ---- eval=F------------------------------------------------------------------
# sig_noise(processed, metric = "run_sig_over_noise") >
# sig_noise(raman_hdpe, metric = "run_sig_over_noise")
## ---- out.width="100%"--------------------------------------------------------
spectral_map_p <- spectral_map |>
process_spec(flatten_range = T)
spectral_map_p$metadata$sig_noise <- sig_noise(spectral_map_p)
heatmap_spec(spectral_map_p, sn = spectral_map_p$metadata$sig_noise, min_sn = 5)
## ---- fig.align="center", out.width="100%"------------------------------------
trans_raman_hdpe <- raman_hdpe
trans_raman_hdpe$spectra <- 2 - trans_raman_hdpe$spectra^2
rev_trans_raman_hdpe <- trans_raman_hdpe |>
adj_intens(type = "transmittance")
plotly_spec(trans_raman_hdpe, rev_trans_raman_hdpe)
## -----------------------------------------------------------------------------
conform_spec(raman_hdpe) |> # default convert res to 5 wavenumbers.
summary()
# Force one spectrum to have the exact same wavenumbers as another
conform_spec(asp_example, range = ps_example$wavenumber, res = NULL) |>
summary()
# Specify the wavenumber resolution and use a rolling join instead of linear
# approximation (faster for large datasets).
conform_spec(spectral_map, range = ps_example$wavenumber, res = 10,
type = "roll") |>
summary()
## ----smooth_intens, fig.cap = "Sample `raman_hdpe` spectrum with different smoothing polynomials.", fig.width=5, fig.align="center"----
none <- make_rel(raman_hdpe)
p1 <- smooth_intens(raman_hdpe, polynomial = 1, derivative = 0, abs = F)
p4 <- smooth_intens(raman_hdpe, polynomial = 4, derivative = 0, abs = F)
c_spec(list(none, p1, p4)) |>
plot()
## ----compare_derivative, fig.cap = "Sample `raman_hdpe` spectrum with different derivatives.", fig.width=5, fig.align="center"----
none <- make_rel(raman_hdpe)
d1 <- smooth_intens(raman_hdpe, derivative = 1, abs = T)
d2 <- smooth_intens(raman_hdpe, derivative = 2, abs = T)
c_spec(list(none, d1, d2)) |>
plot()
## ----subtr_baseline, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020).", fig.width=5, fig.align="center"----
alternative_baseline <- smooth_intens(raman_hdpe, polynomial = 1, window = 51,
derivative = 0, abs = F, make_rel = F) |>
flatten_range(min = 2700, max = 3200, make_rel = F)
none <- make_rel(raman_hdpe)
d2 <- subtr_baseline(raman_hdpe, type = "manual",
baseline = alternative_baseline)
d8 <- subtr_baseline(raman_hdpe, degree = 8)
c_spec(list(none, d2, d8)) |>
plot()
## ----restrict_range, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of range restriction.", fig.width=5, fig.align="center"----
none <- make_rel(raman_hdpe)
r1 <- restrict_range(raman_hdpe, min = 1000, max = 2000)
r2 <- restrict_range(raman_hdpe, min = c(1000, 1800), max = c(1200, 2000))
compare_ranges <- c_spec(list(none, r1, r2), range = "common")
# Common argument crops the ranges to the most common range between the spectra
# when joining.
plot(compare_ranges)
## ----flatten_range, fig.cap = "Sample `raman_hdpe` spectrum with different degrees of background subtraction (Cowger et al., 2020).", fig.width=5, fig.align="center"----
single <- filter_spec(spectral_map, 120) # Function to filter spectra by index
# number or name or a logical vector.
none <- make_rel(single)
f1 <- flatten_range(single) #default flattening the CO2 region.
f2 <- flatten_range(single, min = c(1000, 2500), max = c(1200, 3000))
compare_flats <- c_spec(list(none, f1, f2))
plot(compare_flats)
## ----make_rel, fig.cap = "Sample `raman_hdpe` spectrum with one being relative and the other untransformed."----
relative <- make_rel(raman_hdpe)
## ---- eval=F------------------------------------------------------------------
# get_lib(type = "derivative")
## ---- eval=F------------------------------------------------------------------
# lib <- load_lib(type = "derivative")
## ---- eval = F----------------------------------------------------------------
# data("test_lib")
# data("raman_hdpe")
#
# processed <- process_spec(x = raman_hdpe,
# conform_spec = F, #We will conform during matching.
# smooth_intens = T #Conducts the default derivative transformation.
# )
#
# # Check to make sure that the signal to noise ratio of the processed spectra is
# # greater than 10.
# print(sig_noise(processed) > 10)
# plotly_spec(raman_hdpe, processed)
## ----eval=FALSE---------------------------------------------------------------
# matches <- match_spec(x = processed, library = test_lib, conform = T,
# add_library_metadata = "sample_name", top_n = 5)
# print(matches[,c("object_id", "library_id", "match_val", "SpectrumType",
# "SpectrumIdentity")])
## ----eval=FALSE---------------------------------------------------------------
# get_metadata(x = test_lib, logic = matches[[1,"library_id"]], rm_empty = T)
## ----eval=FALSE---------------------------------------------------------------
# plotly_spec(processed, filter_spec(test_lib, logic = matches[[1,"library_id"]]))
## ---- eval = F----------------------------------------------------------------
# data("test_lib")
# test_map <- read_any(read_extdata("CA_tiny_map.zip"))
#
# test_map_processed <- process_spec(test_map, conform_spec_args = list(
# range = test_lib$wavenumber, res = NULL)
# )
#
# identities <- match_spec(test_map_processed, test_lib, order = test_map,
# add_library_metadata = "sample_name", top_n = 1)
#
# features <- ifelse(identities$match_val > 0.7,
# tolower(identities$polymer_class), "unknown")
#
# id_map <- def_features(x = test_map_processed, features = features)
#
# id_map$metadata$identities <- features
# # Also should probably be implemented automatically in the function when a
# # character value is provided.
#
# # Collapses spectra to their median for each particle
# test_collapsed <- collapse_spec(id_map)
## ---- eval = F----------------------------------------------------------------
# data("test_lib")
# test_map <- read_any(read_extdata("CA_tiny_map.zip"))
#
# # Characterize the total signal as a threshold value.
# snr <- sig_noise(test_map,metric = "log_tot_sig")
#
# # Use this to find your particles and the sig_noise value to use for
# # thresholding.
# heatmap_spec(test_map, z = snr)
#
# # Set define the feature regions based on the threshold. 400 appeared to be
# # where I suspected my particle to be in the previous heatmap.
# id_map <- def_features(x = test_map, features = snr > 400)
#
# # Check that the thresholding worked as expected.
# heatmap_spec(id_map, z = id_map$metadata$feature_id)
#
# # Collapse the spectra to their medians based on the threshold. Important to
# # note here that the particles with id -88 are anything from the FALSE values
# # so they should be your background.
# collapsed_id_map <- id_map |>
# collapse_spec()
#
# # Process the collapsed spectra.
# id_map_processed <- process_spec(collapsed_id_map, conform_spec_args = list(
# range = test_lib$wavenumber, res = NULL)
# )
#
# # Check the spectra.
# plot(id_map_processed)
#
# # Get the matches of the collapsed spectra for the particles.
# matches <- match_spec(id_map_processed, test_lib,
# add_library_metadata = "sample_name", top_n = 1)
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