tests/testthat/_snaps/flux_calc.md

In fluxible: Ecosystem Gas Fluxes Calculations for Closed Loop Chamber Setup

averaging works

Code
  output
Output
  # A tibble: 6 x 6
    f_fluxid f_temp_air_ave datetime            f_flux PAR_ave temp_soil_ave
    <fct>             <dbl> <dttm>               <dbl>   <dbl>         <dbl>
  1 1                  7.31 2022-07-28 23:43:35   95.6    1.95          10.8
  2 2                  7.38 2022-07-28 23:47:22   52.4    2.11          10.7
  3 3                  7.46 2022-07-28 23:52:10   18.6    2.04          10.7
  4 4                  7.77 2022-07-28 23:59:32   69.4    1.84          10.8
  5 5                  7.71 2022-07-29 00:03:10   89.9    1.66          10.6
  6 6                  7.75 2022-07-29 00:06:35   26.2    1.78          12.2

keeping works

Code
  dplyr::select(flux_calc(slopes0, f_slope, datetime, temp_air, conc_unit = "ppm",
    flux_unit = "mmol/m2/h", cols_keep = c("turfID", "type", "f_start"),
    setup_volume = 24.575, atm_pressure = 1, plot_area = 0.0625, cut = FALSE),
  f_fluxid, f_flux, turfID, type, f_start, f_slope)
Message
  Averaging air temperature for each flux...
  Creating a df with the columns from 'cols_keep' argument...
  Calculating fluxes...
  R constant set to 0.082057
  Concentration was measured in ppm
  Fluxes are in mmol/m2/h
Output
  # A tibble: 6 x 6
    f_fluxid f_flux turfID       type  f_start             f_slope
    <fct>     <dbl> <fct>        <fct> <dttm>                <dbl>
  1 1          95.6 156 AN2C 156 ER    2022-07-28 23:43:35   1.56 
  2 2          52.4 74 WN2C 155  NEE   2022-07-28 23:47:22   0.853
  3 3          18.6 74 WN2C 155  ER    2022-07-28 23:52:10   0.303
  4 4          69.4 109 AN3C 109 NEE   2022-07-28 23:59:32   1.13 
  5 5          89.9 109 AN3C 109 ER    2022-07-29 00:03:10   1.46 
  6 6          26.2 29 WN3C 106  NEE   2022-07-29 00:06:35   0.426

keeping and averaging work together

Code
  dplyr::select(flux_calc(slopes0, f_slope, datetime, temp_air, conc_unit = "ppm",
    flux_unit = "mmol/m2/h", cols_keep = c("turfID", "type", "f_start"),
    cols_ave = c("PAR", "temp_soil"), setup_volume = 24.575, atm_pressure = 1,
    plot_area = 0.0625, cut = FALSE), f_fluxid, f_flux, turfID, type, f_start,
  PAR_ave, temp_soil_ave)
Message
  Averaging air temperature for each flux...
  Creating a df with the columns from 'cols_keep' argument...
  Creating a df with the columns from 'cols_ave' argument...
  Calculating fluxes...
  R constant set to 0.082057
  Concentration was measured in ppm
  Fluxes are in mmol/m2/h
Output
  # A tibble: 6 x 7
    f_fluxid f_flux turfID       type  f_start             PAR_ave temp_soil_ave
    <fct>     <dbl> <fct>        <fct> <dttm>                <dbl>         <dbl>
  1 1          95.6 156 AN2C 156 ER    2022-07-28 23:43:35    1.95          10.8
  2 2          52.4 74 WN2C 155  NEE   2022-07-28 23:47:22    2.11          10.7
  3 3          18.6 74 WN2C 155  ER    2022-07-28 23:52:10    2.04          10.7
  4 4          69.4 109 AN3C 109 NEE   2022-07-28 23:59:32    1.84          10.8
  5 5          89.9 109 AN3C 109 ER    2022-07-29 00:03:10    1.66          10.6
  6 6          26.2 29 WN3C 106  NEE   2022-07-29 00:06:35    1.78          12.2

nesting works

Code
  output
Output
  # A tibble: 1,251 x 7
  # Groups:   f_fluxid [6]
     f_fluxid datetime            f_flux PAR_ave temp_soil_ave  conc   PAR
     <fct>    <dttm>               <dbl>   <dbl>         <dbl> <dbl> <dbl>
   1 1        2022-07-28 23:43:35   6.94    1.95          10.8  447. NA   
   2 1        2022-07-28 23:43:35   6.94    1.95          10.8  447.  1.68
   3 1        2022-07-28 23:43:35   6.94    1.95          10.8  448. NA   
   4 1        2022-07-28 23:43:35   6.94    1.95          10.8  449. NA   
   5 1        2022-07-28 23:43:35   6.94    1.95          10.8  449. NA   
   6 1        2022-07-28 23:43:35   6.94    1.95          10.8  450. NA   
   7 1        2022-07-28 23:43:35   6.94    1.95          10.8  451. NA   
   8 1        2022-07-28 23:43:35   6.94    1.95          10.8  451. NA   
   9 1        2022-07-28 23:43:35   6.94    1.95          10.8  453. NA   
  10 1        2022-07-28 23:43:35   6.94    1.95          10.8  453. NA   
  # i 1,241 more rows

nesting all works

Code
  output
Output
  # A tibble: 1,251 x 28
  # Groups:   f_fluxid [6]
     f_fluxid datetime            f_flux PAR_ave temp_soil_ave
     <fct>    <dttm>               <dbl>   <dbl>         <dbl>
   1 1        2022-07-28 23:43:35   6.94    1.95          10.8
   2 1        2022-07-28 23:43:35   6.94    1.95          10.8
   3 1        2022-07-28 23:43:35   6.94    1.95          10.8
   4 1        2022-07-28 23:43:35   6.94    1.95          10.8
   5 1        2022-07-28 23:43:35   6.94    1.95          10.8
   6 1        2022-07-28 23:43:35   6.94    1.95          10.8
   7 1        2022-07-28 23:43:35   6.94    1.95          10.8
   8 1        2022-07-28 23:43:35   6.94    1.95          10.8
   9 1        2022-07-28 23:43:35   6.94    1.95          10.8
  10 1        2022-07-28 23:43:35   6.94    1.95          10.8
  # i 1,241 more rows
  # i 23 more variables: nested_variables_datetime <dttm>,
  #   nested_variables_temp_air <dbl>, nested_variables_temp_soil <dbl>,
  #   nested_variables_conc <dbl>, nested_variables_PAR <dbl>,
  #   nested_variables_turfID <fct>, nested_variables_type <fct>,
  #   nested_variables_f_start <dttm>, nested_variables_f_end <dttm>,
  #   nested_variables_f_ratio <dbl>, nested_variables_f_flag_match <chr>, ...

fahrenheit conversion works

Code
  dplyr::select(flux_calc(slopes0_temp, f_slope, datetime, temp_fahr, conc_unit = "ppm",
    flux_unit = "mmol/m2/h", temp_air_unit = "fahrenheit", setup_volume = 24.575,
    atm_pressure = 1, plot_area = 0.0625, cut = FALSE), f_fluxid, f_temp_air_ave,
  datetime, f_flux)
Message
  Averaging air temperature for each flux...
  Calculating fluxes...
  R constant set to 0.082057
  Concentration was measured in ppm
  Fluxes are in mmol/m2/h
Output
  # A tibble: 6 x 4
    f_fluxid f_temp_air_ave datetime            f_flux
    <fct>             <dbl> <dttm>               <dbl>
  1 1                  45.2 2022-07-28 23:43:35   95.6
  2 2                  45.3 2022-07-28 23:47:22   52.4
  3 3                  45.4 2022-07-28 23:52:10   18.6
  4 4                  46.0 2022-07-28 23:59:32   69.4
  5 5                  45.9 2022-07-29 00:03:10   89.9
  6 6                  45.9 2022-07-29 00:06:35   26.2

kelvin conversion works

Code
  dplyr::select(flux_calc(slopes0_temp, f_slope, datetime, temp_kelvin,
    conc_unit = "ppm", flux_unit = "mmol/m2/h", temp_air_unit = "kelvin",
    setup_volume = 24.575, atm_pressure = 1, plot_area = 0.0625, cut = FALSE),
  f_fluxid, f_temp_air_ave, datetime, f_flux)
Message
  Averaging air temperature for each flux...
  Calculating fluxes...
  R constant set to 0.082057
  Concentration was measured in ppm
  Fluxes are in mmol/m2/h
Output
  # A tibble: 6 x 4
    f_fluxid f_temp_air_ave datetime            f_flux
    <fct>             <dbl> <dttm>               <dbl>
  1 1                  280. 2022-07-28 23:43:35   95.6
  2 2                  281. 2022-07-28 23:47:22   52.4
  3 3                  281. 2022-07-28 23:52:10   18.6
  4 4                  281. 2022-07-28 23:59:32   69.4
  5 5                  281. 2022-07-29 00:03:10   89.9
  6 6                  281. 2022-07-29 00:06:35   26.2

calculating fluxes on dataset with cuts

Code
  dplyr::select(flux_calc(slopes30_flag, f_slope_corr, datetime, temp_air,
    conc_unit = "ppm", flux_unit = "mmol/m2/h", keep_arg = "keep", setup_volume = 24.575,
    atm_pressure = 1, plot_area = 0.0625), f_fluxid, f_temp_air_ave, datetime,
  f_flux)
Message
  Cutting data according to 'keep_arg'...
  Averaging air temperature for each flux...
  Calculating fluxes...
  R constant set to 0.082057
  Concentration was measured in ppm
  Fluxes are in mmol/m2/h
Output
  # A tibble: 6 x 4
    f_fluxid f_temp_air_ave datetime            f_flux
    <fct>             <dbl> <dttm>               <dbl>
  1 1                  7.29 2022-07-28 23:43:35   47.7
  2 2                  7.37 2022-07-28 23:47:22   31.0
  3 3                  7.45 2022-07-28 23:52:10   20.7
  4 4                  7.77 2022-07-28 23:59:32   41.5
  5 5                  7.70 2022-07-29 00:03:10    0  
  6 6                  7.74 2022-07-29 00:06:35   26.1

volume can be a variable instead of a constant

Code
  dplyr::select(flux_calc(slopes0_vol, f_slope, datetime, temp_air, setup_volume = volume,
    conc_unit = "ppm", flux_unit = "mmol/m2/h", atm_pressure = 1, plot_area = 0.0625),
  f_fluxid, f_temp_air_ave, datetime, f_flux)
Message
  Cutting data according to 'keep_arg'...
  Averaging air temperature for each flux...
  Calculating fluxes...
  R constant set to 0.082057
  Concentration was measured in ppm
  Fluxes are in mmol/m2/h
Output
  # A tibble: 6 x 4
    f_fluxid f_temp_air_ave datetime            f_flux
    <fct>             <dbl> <dttm>               <dbl>
  1 1                  7.31 2022-07-28 23:43:35   70.4
  2 2                  7.38 2022-07-28 23:47:22   59.9
  3 3                  7.46 2022-07-28 23:52:10   15.2
  4 4                  7.77 2022-07-28 23:59:32   68.0
  5 5                  7.71 2022-07-29 00:03:10   14.9
  6 6                  7.75 2022-07-29 00:06:35   37.3

Fluxible workflow works from start to finish

Code
  str(fluxes_test)
Output
  tibble [6 x 6] (S3: tbl_df/tbl/data.frame)
   $ f_fluxid      : Factor w/ 6 levels "1","2","3","4",..: 1 2 3 4 5 6
   $ f_slope_corr  : num [1:6] 0.785 0.503 0.344 0.693 0 ...
   $ f_temp_air_ave: num [1:6] 7.28 7.37 7.45 7.77 7.69 ...
   $ datetime      : POSIXct[1:6], format: "2022-07-28 23:43:35" "2022-07-28 23:47:22" ...
   $ f_flux        : num [1:6] 48.3 30.9 21.1 42.5 0 ...
   $ f_model       : chr [1:6] "exp_zhao18" "exp_zhao18" "exp_zhao18" "exp_zhao18" ...

Stupeflux works with slope_correction = FALSE

Code
  stupeflux(raw_conc = co2_df_short, field_record = record_short, f_datetime = datetime,
    start_col = start, f_conc = conc, start_cut = 10, measurement_length = 180,
    fit_type = "exp_zhao18", temp_air_col = temp_air, conc_unit = "ppm",
    flux_unit = "mmol/m2/h", setup_volume = 24.575, atm_pressure = 1, plot_area = 0.0625,
    slope_correction = FALSE)
Message
  Cutting measurements...
  Estimating starting parameters for optimization...
  Optimizing fitting parameters...
  Calculating fits and slopes...
  Done.

   Total number of measurements: 6

   ok    5   83 %
   zero      1   17 %
   discard   0   0 %
   force_discard     0   0 %
   start_error   0   0 %
   no_data   0   0 %
   force_ok      0   0 %
   force_zero    0   0 %
   force_lm      0   0 %
   no_slope      0   0 %
  Cutting data according to 'keep_arg'...
  Averaging air temperature for each flux...
  Calculating fluxes...
  R constant set to 0.082057
  Concentration was measured in ppm
  Fluxes are in mmol/m2/h
Output
  # A tibble: 6 x 6
    f_fluxid f_slope f_temp_air_ave datetime            f_flux f_model   
    <fct>      <dbl>          <dbl> <dttm>               <dbl> <chr>     
  1 1          0.785           7.28 2022-07-28 23:43:35   48.3 exp_zhao18
  2 2          0.503           7.37 2022-07-28 23:47:22   30.9 exp_zhao18
  3 3          0.344           7.45 2022-07-28 23:52:10   21.1 exp_zhao18
  4 4          0.693           7.77 2022-07-28 23:59:32   42.5 exp_zhao18
  5 5          1.20            7.69 2022-07-29 00:03:10   74.0 exp_zhao18
  6 6          0.433           7.74 2022-07-29 00:06:35   26.6 exp_zhao18

Fluxible workflow works with kappamax

Code
  fluxes_test
Output
  # A tibble: 6 x 4
    f_model f_temp_air_ave datetime            f_flux
    <chr>            <dbl> <dttm>               <dbl>
  1 exp_hm            7.29 2022-07-28 23:43:35   44.7
  2 exp_hm            7.37 2022-07-28 23:47:22   25.7
  3 exp_hm            7.45 2022-07-28 23:52:10   23.0
  4 exp_hm            7.77 2022-07-28 23:59:32   44.5
  5 linear            7.70 2022-07-29 00:03:10    0  
  6 exp_hm            7.74 2022-07-29 00:06:35   24.8

Working with two gases

Code
  str(fluxes_twogases)
Output
  tibble [12 x 7] (S3: tbl_df/tbl/data.frame)
   $ f_quality_flag: chr [1:12] "ok" "ok" "ok" "ok" ...
   $ f_fluxid      : Factor w/ 12 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
   $ f_temp_air_ave: num [1:12] 13.4 16.5 17.1 14.4 15 ...
   $ datetime      : POSIXct[1:12], format: "2024-06-18 10:04:37" "2024-06-18 11:12:52" ...
   $ flux_co2      : num [1:12] 0.08292 0.38505 0.43518 0.00108 0.06371 ...
   $ f_model       : chr [1:12] "exp_zhao18" "exp_zhao18" "exp_zhao18" "exp_zhao18" ...
   $ flux_ch4      : num [1:12] -0.04873 0.01165 0 -0.00649 0 ...
   - attr(*, "fit_type")= chr "exp_zhao18"

sum and median works

Code
  output
Output
  # A tibble: 6 x 6
    f_fluxid f_temp_air_ave datetime            f_flux PAR_sum temp_soil_med
    <fct>             <dbl> <dttm>               <dbl>   <dbl>         <dbl>
  1 1                  7.31 2022-07-28 23:43:35   95.6    40.9          10.8
  2 2                  7.38 2022-07-28 23:47:22   52.4    44.2          10.7
  3 3                  7.46 2022-07-28 23:52:10   18.6    42.7          10.7
  4 4                  7.77 2022-07-28 23:59:32   69.4    38.6          10.8
  5 5                  7.71 2022-07-29 00:03:10   89.9    33.3          10.6
  6 6                  7.75 2022-07-29 00:06:35   26.2    37.4          12.2

sum and average works on same variable

Code
  output
Output
  # A tibble: 6 x 6
    f_fluxid datetime            f_flux PAR_sum temp_soil_ave PAR_ave
    <fct>    <dttm>               <dbl>   <dbl>         <dbl>   <dbl>
  1 1        2022-07-28 23:43:35   95.6    40.9          10.8    1.95
  2 2        2022-07-28 23:47:22   52.4    44.2          10.7    2.11
  3 3        2022-07-28 23:52:10   18.6    42.7          10.7    2.04
  4 4        2022-07-28 23:59:32   69.4    38.6          10.8    1.84
  5 5        2022-07-29 00:03:10   89.9    33.3          10.6    1.66
  6 6        2022-07-29 00:06:35   26.2    37.4          12.2    1.78

fluxible documentation built on June 25, 2025, 1:08 a.m.