Plot spectrum from 1D fit

Usage

plot_sparse_1d(
  fit_data,
  tables = NULL,
  spec_idx = 1,
  col_model = 2,
  col_resonance = NULL,
  lwd = 1,
  tick_spacing = 0.02,
  coupling_spacing = 0.01,
  coupling_marks = 0.009,
  xaxs = "i",
  yaxt = "n",
  bty = "n",
  always_show_start = FALSE,
  add = FALSE,
  ppm_map = make_map(get_spec_int(fit_data, "input", spec_idx)[[1]])
)

Arguments

fit_data: fit_input or fit_output structure
tables: list with resonances, nuclei, and couplings tables
spec_idx: index of spectrum to plot
col_model: color for modeled peak shapes
col_resonance: colors for resonances
lwd: line width
tick_spacing: spacing between axis ticks (ppm)
coupling_spacing: spacing between coupling annotations (fraction of plot)
coupling_marks: size of coupling tick marks (fraction of plot)
xaxs: x-axis style passed to plot
yaxt: y-axis style passed to plot
bty: box type passed to plot
always_show_start: logical indicating whether to show starting model when fit is present
add: logical indicating whether to add to existing plot
ppm_map: sparse axis map created by make_map

Value

No return value, called for side effects (draws a plot).

Examples

spec_file <- system.file("extdata", "tyrosine", "proton.ft1", package = "fitnmr")
spec <- read_nmrpipe(spec_file)

start_resonances <- structure(
  list(
    x = c("HA", "HB3", "HB2", "HD1/2", "HE1/2"),
    x_sc = c(
      "HA-HB3 HA-HB2",
      "HA-HB3 HB3-HB2",
      "HA-HB2 HB3-HB2",
      "HD1/2-HE1/2-3 HD1/2-HE1/2-5",
      "HD1/2-HE1/2-3 HD1/2-HE1/2-5"
    )
  ),
  row.names = c("HA", "HB3", "HB2", "HD1/2", "HE1/2"),
  class = "data.frame"
)

start_nuclei <- structure(
  list(
    omega0_ppm = c(3.364, 2.635, 2.799, 6.94, 6.538),
    r2_hz = c(0.7, 0.7, 0.7, 0.7, 0.7)
  ),
  class = "data.frame",
  row.names = c("HA", "HB3", "HB2", "HD1/2", "HE1/2")
)

start_couplings <- structure(
  list(hz = c(7.153, 5.159, -13.941, 7.7, 2)),
  class = "data.frame",
  row.names = c("HA-HB3", "HA-HB2", "HB3-HB2", "HD1/2-HE1/2-3", "HD1/2-HE1/2-5")
)

start_tables <- list(
  resonances = start_resonances,
  nuclei = start_nuclei,
  couplings = start_couplings
)
param_list <- tables_to_param_list(list(spec), start_tables)
param_list$start_list$m0[] <- 1e9
arg_list <- param_list_to_arg_list(param_list)
fit_input <- do.call(make_fit_input, c(list(list(spec), omega0_plus = 0.075), arg_list))
fit_output <- perform_fit(fit_input)
plot_sparse_1d(fit_output, start_tables)