---
title: "aircraft-flask"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{aircraft-flask}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(rtorf)
library(data.table)
```


```{r read1, eval = F}

cate = c("aircraft-pfp",
         "aircraft-insitu",
         "aircraft-flask",
         "surface-insitu",
         "surface-flask", 
         "surface-pfp",   
         "tower-insitu",  
         "aircore",       
         "shipboard-insitu",
         "shipboard-flask") 

obs <- "Z:/obspack/obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08/data/nc/"
index <- obs_summary(obs = obs, 
                     categories = cate)
```

```
#> Number of files of index: 479
#>               sector     N
#>               <char> <int>
#>  1:     aircraft-pfp    42
#>  2:  aircraft-insitu    17
#>  3:    surface-flask   106
#>  4:   surface-insitu   121
#>  5:   aircraft-flask     4
#>  6:          aircore     1
#>  7:      surface-pfp    33
#>  8:     tower-insitu   150
#>  9:  shipboard-flask     4
#> 10: shipboard-insitu     1
#> 11:    Total sectors   479
#> Detected 203 files with agl
#> Detected 276 files without agl
```

Now we read the `aircraft-flask` using the function `obs_read_nc`. To this date,
`solar_time` is not included for aircraft, so we `FALSE` that argument.

```{r readnc, eval = F}
datasetid <- "aircraft-flask"
df <- obs_read_nc(index = index,
                  categories = datasetid,
                  solar_time = FALSE,
                  verbose = T)

```

```
#> Searching aircraft-flask...
#> 1: ch4_aia_aircraft-flask_2_representative.nc
#> 2: ch4_aoa_aircraft-flask_19_allvalid.nc
#> 3: ch4_con_aircraft-flask_42_allvalid.nc
#> 4: ch4_iagos-caribic_aircraft-flask_457_allvalid.nc
```

Now we check the data

```{r checkdf, eval = F}
df
```

```
#>         year month   day  hour minute second       time start_time
#>        <int> <int> <int> <int>  <int>  <int>      <int>      <int>
#>     1:  1991     6    24     0      5      0  677721900  677721900
#>     2:  1991     6    24     0     18      0  677722680  677722680
#>     3:  1991     6    24     0     20      0  677722800  677722800
#>     4:  1991     6    24     0     45      0  677724300  677724300
#>     5:  1991     6    24     0     50      0  677724600  677724600
#>    ---                                                            
#> 16351:  2020     3     4    14     49      4 1583333344 1583333344
#> 16352:  2020     3     4    15     33      4 1583335984 1583335984
#> 16353:  2020     3     4    16     17      2 1583338622 1583338622
#> 16354:  2020     3     4    17      1      8 1583341268 1583341268
#> 16355:  2020     3     4    17     45      7 1583343907 1583343907
#>        midpoint_time             datetime time_decimal time_interval
#>                <int>               <char>        <num>         <int>
#>     1:     677721900 1991-06-24T00:05:00Z     1991.477          3600
#>     2:     677722680 1991-06-24T00:18:00Z     1991.477          3600
#>     3:     677722800 1991-06-24T00:20:00Z     1991.477          3600
#>     4:     677724300 1991-06-24T00:45:00Z     1991.477          3600
#>     5:     677724600 1991-06-24T00:50:00Z     1991.477          3600
#>    ---                                                              
#> 16351:    1583333344 2020-03-04T14:49:04Z     2020.174            NA
#> 16352:    1583335984 2020-03-04T15:33:04Z     2020.174            NA
#> 16353:    1583338622 2020-03-04T16:17:02Z     2020.174            NA
#> 16354:    1583341268 2020-03-04T17:01:08Z     2020.174            NA
#> 16355:    1583343907 2020-03-04T17:45:07Z     2020.174            NA
#>               value nvalue value_std_dev latitude longitude altitude elevation
#>               <num>  <int>         <num>    <num>     <num>    <num>     <num>
#>     1: 1.686550e-06      1            NA  -38.931   145.152     4270         0
#>     2: 1.684910e-06      1            NA  -39.436   145.276     4270         0
#>     3: 1.692130e-06      1            NA  -39.404   145.268     4270         0
#>     4: 1.682670e-06      1            NA  -40.394   144.721     4270         0
#>     5: 1.683830e-06      1            NA  -40.699   144.710     4270         0
#>    ---                                                                        
#> 16351: 1.862239e-06     NA            NA   21.430    11.310    12040        NA
#> 16352: 1.861981e-06     NA            NA   26.210     9.000    11970        NA
#> 16353: 1.856997e-06     NA            NA   31.120     8.940    11870        NA
#> 16354: 1.858964e-06     NA            NA   36.020    10.450    12380        NA
#> 16355: 1.858041e-06     NA            NA   41.150    11.560    12280        NA
#>        qcflag obs_flag obspack_num
#>        <char>    <int>       <int>
#>     1:    ...        1     4504993
#>     2:    ...        1     4504994
#>     3:    ...        1     4504995
#>     4:    ...        1     4504996
#>     5:    ...        1     4504997
#>    ---                            
#> 16351:   <NA>        1     8431291
#> 16352:   <NA>        1     8431292
#> 16353:   <NA>        1     8431293
#> 16354:   <NA>        1     8431294
#> 16355:   <NA>        1     8431295
#>                                                                                                obspack_id
#>                                                                                                    <char>
#>     1:       obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504993
#>     2:       obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504994
#>     3:       obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504995
#>     4:       obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504996
#>     5:       obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_aia_aircraft-flask_2_representative~4504997
#>    ---                                                                                                   
#> 16351: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431291
#> 16352: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431292
#> 16353: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431293
#> 16354: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431294
#> 16355: obspack_ch4_1_GLOBALVIEWplus_v5.1_2023-03-08~ch4_iagos-caribic_aircraft-flask_457_allvalid~8431295
#>        unique_sample_location_num          scale site_elevation_unit
#>                             <int>         <char>              <char>
#>     1:                    5959263 WMO CH4 X2004A                masl
#>     2:                    5959132 WMO CH4 X2004A                masl
#>     3:                    5959137 WMO CH4 X2004A                masl
#>     4:                    5958950 WMO CH4 X2004A                masl
#>     5:                    5957693 WMO CH4 X2004A                masl
#>    ---                                                              
#> 16351:                   52172274 WMO CH4 X2004A                <NA>
#> 16352:                   52172545 WMO CH4 X2004A                <NA>
#> 16353:                   52172779 WMO CH4 X2004A                <NA>
#> 16354:                   52173055 WMO CH4 X2004A                <NA>
#> 16355:                   52173483 WMO CH4 X2004A                <NA>
#>        dataset_project dataset_selection_tag
#>                 <char>                <char>
#>     1:  aircraft-flask        representative
#>     2:  aircraft-flask        representative
#>     3:  aircraft-flask        representative
#>     4:  aircraft-flask        representative
#>     5:  aircraft-flask        representative
#>    ---                                      
#> 16351:  aircraft-flask              allvalid
#> 16352:  aircraft-flask              allvalid
#> 16353:  aircraft-flask              allvalid
#> 16354:  aircraft-flask              allvalid
#> 16355:  aircraft-flask              allvalid
#>                                                site_name site_elevation
#>                                                   <char>          <num>
#>     1:                   Bass Strait/Cape Grim, Tasmania          0e+00
#>     2:                   Bass Strait/Cape Grim, Tasmania          0e+00
#>     3:                   Bass Strait/Cape Grim, Tasmania          0e+00
#>     4:                   Bass Strait/Cape Grim, Tasmania          0e+00
#>     5:                   Bass Strait/Cape Grim, Tasmania          0e+00
#>    ---                                                                 
#> 16351: In-service Aircraft for a Global Observing System         -1e+34
#> 16352: In-service Aircraft for a Global Observing System         -1e+34
#> 16353: In-service Aircraft for a Global Observing System         -1e+34
#> 16354: In-service Aircraft for a Global Observing System         -1e+34
#> 16355: In-service Aircraft for a Global Observing System         -1e+34
#>        site_latitude site_longitude site_country site_code site_utc2lst
#>                <num>          <num>       <char>    <char>        <num>
#>     1:    -4.053e+01      1.443e+02    Australia       AIA           10
#>     2:    -4.053e+01      1.443e+02    Australia       AIA           10
#>     3:    -4.053e+01      1.443e+02    Australia       AIA           10
#>     4:    -4.053e+01      1.443e+02    Australia       AIA           10
#>     5:    -4.053e+01      1.443e+02    Australia       AIA           10
#>    ---                                                                 
#> 16351:    -1.000e+34     -1.000e+34         <NA>     IAGOS           NA
#> 16352:    -1.000e+34     -1.000e+34         <NA>     IAGOS           NA
#> 16353:    -1.000e+34     -1.000e+34         <NA>     IAGOS           NA
#> 16354:    -1.000e+34     -1.000e+34         <NA>     IAGOS           NA
#> 16355:    -1.000e+34     -1.000e+34         <NA>     IAGOS           NA
#>         lab_1_abbr dataset_calibration_scale altitude_final type_altitude
#>             <char>                    <char>          <num>         <num>
#>     1:       CSIRO            WMO CH4 X2004A           4270             1
#>     2:       CSIRO            WMO CH4 X2004A           4270             1
#>     3:       CSIRO            WMO CH4 X2004A           4270             1
#>     4:       CSIRO            WMO CH4 X2004A           4270             1
#>     5:       CSIRO            WMO CH4 X2004A           4270             1
#>    ---                                                                   
#> 16351: KIT/IMK-ASF            WMO CH4 X2004A          12040             1
#> 16352: KIT/IMK-ASF            WMO CH4 X2004A          11970             1
#> 16353: KIT/IMK-ASF            WMO CH4 X2004A          11870             1
#> 16354: KIT/IMK-ASF            WMO CH4 X2004A          12380             1
#> 16355: KIT/IMK-ASF            WMO CH4 X2004A          12280             1
#>        instrument intake_height method pressure
#>            <char>         <num> <char>    <num>
#>     1:       <NA>            NA   <NA>       NA
#>     2:       <NA>            NA   <NA>       NA
#>     3:       <NA>            NA   <NA>       NA
#>     4:       <NA>            NA   <NA>       NA
#>     5:       <NA>            NA   <NA>       NA
#>    ---                                         
#> 16351:       <NA>            NA   <NA>   205.08
#> 16352:       <NA>            NA   <NA>   205.06
#> 16353:       <NA>            NA   <NA>   205.07
#> 16354:       <NA>            NA   <NA>   185.72
#> 16355:       <NA>            NA   <NA>   185.68
#>                                  source_id value_unc
#>                                     <char>     <num>
#>     1:                                <NA>        NA
#>     2:                                <NA>        NA
#>     3:                                <NA>        NA
#>     4:                                <NA>        NA
#>     5:                                <NA>        NA
#>    ---                                              
#> 16351: GHG_20200304_591_CPT_MUC_V02.txt~83 8.846e-10
#> 16352: GHG_20200304_591_CPT_MUC_V02.txt~84 8.844e-10
#> 16353: GHG_20200304_591_CPT_MUC_V02.txt~85 8.821e-10
#> 16354: GHG_20200304_591_CPT_MUC_V02.txt~86 8.830e-10
#> 16355: GHG_20200304_591_CPT_MUC_V02.txt~87 8.826e-10
```


Now we can process the data. We first filter for observations
within our spatial domain:

## Checks and definitions

```{r spatial, eval = F}
north <- 80
south <- 10
west <- -170
east <- -50
max_altitude <- 8000
yy <- 2022

```

We check altitude, intake_height, altitude_final and elevation.
altitude_final is a column from intake_height, added to match
column from obs_read text files.

```{r checkcols, eval = F}
df[, c("altitude", "altitude_final", "intake_height", "elevation")]
```

```
#>        altitude altitude_final intake_height elevation
#>           <num>          <num>         <num>     <num>
#>     1:     4270           4270            NA         0
#>     2:     4270           4270            NA         0
#>     3:     4270           4270            NA         0
#>     4:     4270           4270            NA         0
#>     5:     4270           4270            NA         0
#>    ---                                                
#> 16351:    12040          12040            NA        NA
#> 16352:    11970          11970            NA        NA
#> 16353:    11870          11870            NA        NA
#> 16354:    12380          12380            NA        NA
#> 16355:    12280          12280            NA        NA
```

The temporal range of data is
```{r range_year, eval = F}
range(df$year)
```

```
#> [1] 1991 2022
```

We also check for dimensions of data

```{r dim_df, eval = F}
dim(df)
```

```
#> [1] 16355    45
```

## Filters

```{r spatial_temporal_filter, eval = F}
df <- df[year == yy]

# df <- df[altitude_final < max_altitude &
#            latitude < north &
#            latitude > south &
#            longitude < east &
#            longitude > west]
dim(df)
```


## Adding time as POSIXct class

```{r add_time1, eval = F}
df <- obs_addtime(df)
df[, "timeUTC"]
```

```
#>                  timeUTC
#>                   <POSc>
#>   1: 2020-01-22 00:01:30
#>   2: 2020-01-22 00:09:30
#>   3: 2020-01-22 00:19:30
#>   4: 2020-01-22 00:30:30
#>   5: 2020-01-22 00:39:30
#>  ---                    
#> 413: 2020-03-04 14:49:04
#> 414: 2020-03-04 15:33:04
#> 415: 2020-03-04 16:17:02
#> 416: 2020-03-04 17:01:08
#> 417: 2020-03-04 17:45:07
```

## Cut time

now we can cut time every 20 seconds. We can chosse other frequency as well.

```{r cut_sec, eval = F}
df$sec2 <- obs_freq(x = df$second,
                     freq = seq(0, 59, 20))
df[, c("second", "sec2")]
```

```
#>      second  sec2
#>       <int> <num>
#>   1:     30    20
#>   2:     30    20
#>   3:     30    20
#>   4:     30    20
#>   5:     30    20
#>  ---             
#> 413:      4     0
#> 414:      4     0
#> 415:      2     0
#> 416:      8     0
#> 417:      7     0
```

## Aggregate data

now we need to add the column key_time, that it will be used
to aggregate other variables

```{r key_time, eval = F}
df$key_time <- ISOdatetime(year = df$year,
                           month = df$month,
                           day = df$day,
                           hour = df$hour,
                           min = df$minute,
                           sec = df$sec2,
                           tz = "UTC")
df[, c("timeUTC", "key_time")]

```

```
#>                  timeUTC            key_time
#>                   <POSc>              <POSc>
#>   1: 2020-01-22 00:01:30 2020-01-22 00:01:20
#>   2: 2020-01-22 00:09:30 2020-01-22 00:09:20
#>   3: 2020-01-22 00:19:30 2020-01-22 00:19:20
#>   4: 2020-01-22 00:30:30 2020-01-22 00:30:20
#>   5: 2020-01-22 00:39:30 2020-01-22 00:39:20
#>  ---                                        
#> 413: 2020-03-04 14:49:04 2020-03-04 14:49:00
#> 414: 2020-03-04 15:33:04 2020-03-04 15:33:00
#> 415: 2020-03-04 16:17:02 2020-03-04 16:17:00
#> 416: 2020-03-04 17:01:08 2020-03-04 17:01:00
#> 417: 2020-03-04 17:45:07 2020-03-04 17:45:00
```
         
```{r aggregatingdata, eval = F}
df2 <- obs_agg(df, cols =  c("year",
                             "month",
                             "day",
                             "hour",
                             "minute",
                             "second",
                             "time",
                             "time_decimal",
                             "value",
                             "latitude",
                             "longitude",
                             "altitude_final",
                             "elevation",
                             "intake_height",
                             # "gps_altitude",
                             # "pressure",
                             # "pressure_altitude",
                             # "u", "v", "temperature",
                             "type_altitude"))
```
## Add local time

Now we add local time

```{r addlt, eval = F}
df3 <- obs_addltime(df2)
setorderv(df3, cols = c("site_code", "timeUTC"),
          order = c(-1, 1))
df3
```

```
#>                  timeUTC site_code  lab_1_abbr dataset_calibration_scale  year
#>                   <POSc>    <char>      <char>                    <char> <int>
#>   1: 2020-01-09 02:14:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>   2: 2020-01-09 02:58:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>   3: 2020-01-09 03:42:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>   4: 2020-01-09 04:26:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>   5: 2020-01-09 05:10:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>  ---                                                                          
#> 413: 2020-12-10 04:48:20       AOA         JMA            WMO CH4 X2004A  2020
#> 414: 2020-12-10 05:01:20       AOA         JMA            WMO CH4 X2004A  2020
#> 415: 2020-12-10 05:11:20       AOA         JMA            WMO CH4 X2004A  2020
#> 416: 2020-12-10 05:23:20       AOA         JMA            WMO CH4 X2004A  2020
#> 417: 2020-12-10 05:34:20       AOA         JMA            WMO CH4 X2004A  2020
#>      month    day  hour minute second       time time_decimal        value
#>      <int> <char> <int>  <int>  <int>      <num>        <num>        <num>
#>   1:     1     09     2     14     20 1578536060     2020.022 1.897204e-06
#>   2:     1     09     2     58     20 1578538700     2020.022 1.879403e-06
#>   3:     1     09     3     42     20 1578541340     2020.022 1.874217e-06
#>   4:     1     09     4     26     20 1578543980     2020.022 1.900391e-06
#>   5:     1     09     5     10     20 1578546620     2020.022 1.878125e-06
#>  ---                                                                      
#> 413:    12     10     4     48     20 1607575700     2020.940 1.918800e-06
#> 414:    12     10     5      1     20 1607576480     2020.940 1.901700e-06
#> 415:    12     10     5     11     20 1607577080     2020.940 1.916000e-06
#> 416:    12     10     5     23     20 1607577800     2020.941 1.926800e-06
#> 417:    12     10     5     34     20 1607578460     2020.941 1.929900e-06
#>      latitude longitude altitude_final elevation intake_height type_altitude
#>         <num>     <num>          <num>     <num>         <num>         <num>
#>   1:   35.150  -116.260          10340       NaN           NaN             1
#>   2:   38.900  -110.190          10550       NaN           NaN             1
#>   3:   43.410  -104.470          10460       NaN           NaN             1
#>   4:   47.810   -97.360          10400       NaN           NaN             1
#>   5:   51.860   -89.110          10260       NaN           NaN             1
#>  ---                                                                        
#> 413:   30.093   139.568           7000  -999.999      -999.999             1
#> 414:   31.069   139.712           7600  -999.999      -999.999             1
#> 415:   31.870   139.856           7600  -999.999      -999.999             1
#> 416:   32.814   139.976           7500  -999.999      -999.999             1
#> 417:   33.746   140.312           5100  -999.999      -999.999             1
#>               local_time    lh
#>                   <POSc> <int>
#>   1: 2020-01-08 18:29:17    18
#>   2: 2020-01-08 19:37:34    19
#>   3: 2020-01-08 20:44:27    20
#>   4: 2020-01-08 21:56:53    21
#>   5: 2020-01-08 23:13:53    23
#>  ---                          
#> 413: 2020-12-10 14:06:36    14
#> 414: 2020-12-10 14:20:10    14
#> 415: 2020-12-10 14:30:45    14
#> 416: 2020-12-10 14:43:14    14
#> 417: 2020-12-10 14:55:34    14
```

## Master

Before generating the receptors list, we have the databe with all the required information

```{r rename, eval = F}
master <- df3

```
We may replace missing values with a nine nines. Here is commented

#master[is.na(master)] <- 999999999

We transform the time variables to character and round coordinates with 4 digits

```{r round, eval = F}
master$timeUTC <- as.character(master$timeUTC)
master$local_time <- as.character(master$local_time)
master$latitude <- round(master$latitude, 4)
master$longitude <- round(master$longitude, 4)

```

## Save master
Finally we save the master file

```{r outfile, eval = F}
out <- tempfile()

```

### txt

```{r outtxt, eval = F}
message(paste0(out,"_", datasetid, ".txt\n"))
fwrite(master,
       paste0(out,"_", datasetid, ".txt"),
       sep = " ")
```

```
#> C:\Users\sibarrae\AppData\Local\Temp\RtmpIP4sJV\file71b065447a30_aircraft-flask.txt
```

### csv

```{r outcsv, eval = F}
message(paste0(out,"_", datasetid, ".csv\n"))
fwrite(master,
       paste0(out,"_", datasetid, ".csv"),
       sep = ",")
```

```
#> C:\Users\sibarrae\AppData\Local\Temp\RtmpIP4sJV\file71b065447a30_aircraft-flask.csv
```

### csvy

CSVY are csv files with a YAML header to include metadata in tabulated
text files

```{r csvy, eval = F}
cat("\nAdding notes in csvy:\n")
notes <- c(paste0("sector: ", datasetid),
           paste0("timespan: ", yy),
           paste0("spatial_limits: north = ", north, ", south = ", south, ", east = ", east, ", west = ", west),
           "data: Data averaged every 20 seconds",
           paste0("altitude: < ", max_altitude),
           "hours: All",
           "local_time: if var `site_utc2lst` is not available, calculated as",
           "longitude/15*60*60 (John Miller)")

cat(notes, sep = "\n")

message(paste0(out,"_", datasetid, ".csvy\n"))
obs_write_csvy(dt = master,
               notes = notes,
               out = paste0(out,"_", datasetid, ".csvy"))
```

```
#> sector: aircraft-flask
#> timespan: 2020
#> spatial_limits: north = 80, south = 10, east = -50, west = -170
#> data: Data averaged every 20 seconds
#> altitude: < 8000
#> hours: All
#> local_time: if var `site_utc2lst` is not available, calculated as
#> longitude/15*60*60 (John Miller)
#> C:\Users\sibarrae\AppData\Local\Temp\RtmpIP4sJV\file71b065447a30_aircraft-flask.csvy

```


```{r readcsvy, eval = F}
obs_read_csvy(paste0(out,"_", datasetid, ".csvy"))
```

```
#>  [1] "---"                                                               
#>  [2] "name: Metadata "                                                   
#>  [3] "sector: aircraft-flask"                                            
#>  [4] "timespan: 2020"                                                    
#>  [5] "spatial_limits: north = 80, south = 10, east = -50, west = -170"   
#>  [6] "data: Data averaged every 20 seconds"                              
#>  [7] "altitude: < 8000"                                                  
#>  [8] "hours: All"                                                        
#>  [9] "local_time: if var `site_utc2lst` is not available, calculated as" 
#> [10] "longitude/15*60*60 (John Miller)"                                  
#> [11] "structure: "                                                       
#> [12] "Classes 'data.table' and 'data.frame':\t417 obs. of  21 variables:"
#> [13] " $ timeUTC                  : chr  \"2020-01-09 02:14:20\" \".."   
#> [14] " $ site_code                : chr  \"IAGOS\" \"IAGOS\" ..."        
#> [15] " $ lab_1_abbr               : chr  \"KIT/IMK-ASF\" \"KIT/IMK\".."  
#> [16] " $ dataset_calibration_scale: chr  \"WMO CH4 X2004A\" \"WMO \".."  
#> [17] " $ year                     : int  2020 2020 2020 2020 202.."      
#> [18] " $ month                    : int  1 1 1 1 1 ..."                  
#> [19] " $ day                      : chr  \"09\" \"09\" ..."              
#> [20] " $ hour                     : int  2 2 3 4 5 ..."                  
#> [21] " $ minute                   : int  14 58 42 26 10 ..."             
#> [22] " $ second                   : int  20 20 20 20 20 ..."             
#> [23] " $ time                     : num  1.58e+09 1.58e+09 ..."          
#> [24] " $ time_decimal             : num  2020 2020 ..."                  
#> [25] " $ value                    : num  1.90e-06 1.88e-06 ..."          
#> [26] " $ latitude                 : num  35.1 38.9 ..."                  
#> [27] " $ longitude                : num  -116 -110 ..."                  
#> [28] " $ altitude_final           : num  10340 10550 ..."                
#> [29] " $ elevation                : num  NaN NaN NaN NaN NaN ..."        
#> [30] " $ intake_height            : num  NaN NaN NaN NaN NaN ..."        
#> [31] " $ type_altitude            : num  1 1 1 1 1 ..."                  
#> [32] " $ local_time               : chr  \"2020-01-08 18:29:17.5\".."    
#> [33] " $ lh                       : int  18 19 20 21 23 ..."             
#> [34] " - attr(*, \".internal.selfref\")=<externalptr> "                  
#> [35] "NULL"                                                              
#> [36] "---"
#>                  timeUTC site_code  lab_1_abbr dataset_calibration_scale  year
#>                   <POSc>    <char>      <char>                    <char> <int>
#>   1: 2020-01-09 02:14:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>   2: 2020-01-09 02:58:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>   3: 2020-01-09 03:42:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>   4: 2020-01-09 04:26:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>   5: 2020-01-09 05:10:20     IAGOS KIT/IMK-ASF            WMO CH4 X2004A  2020
#>  ---                                                                          
#> 413: 2020-12-10 04:48:20       AOA         JMA            WMO CH4 X2004A  2020
#> 414: 2020-12-10 05:01:20       AOA         JMA            WMO CH4 X2004A  2020
#> 415: 2020-12-10 05:11:20       AOA         JMA            WMO CH4 X2004A  2020
#> 416: 2020-12-10 05:23:20       AOA         JMA            WMO CH4 X2004A  2020
#> 417: 2020-12-10 05:34:20       AOA         JMA            WMO CH4 X2004A  2020
#>      month   day  hour minute second       time time_decimal        value
#>      <int> <int> <int>  <int>  <int>      <int>        <num>        <num>
#>   1:     1     9     2     14     20 1578536060     2020.022 1.897204e-06
#>   2:     1     9     2     58     20 1578538700     2020.022 1.879403e-06
#>   3:     1     9     3     42     20 1578541340     2020.022 1.874217e-06
#>   4:     1     9     4     26     20 1578543980     2020.022 1.900391e-06
#>   5:     1     9     5     10     20 1578546620     2020.022 1.878125e-06
#>  ---                                                                     
#> 413:    12    10     4     48     20 1607575700     2020.940 1.918800e-06
#> 414:    12    10     5      1     20 1607576480     2020.940 1.901700e-06
#> 415:    12    10     5     11     20 1607577080     2020.940 1.916000e-06
#> 416:    12    10     5     23     20 1607577800     2020.941 1.926800e-06
#> 417:    12    10     5     34     20 1607578460     2020.941 1.929900e-06
#>      latitude longitude altitude_final elevation intake_height type_altitude
#>         <num>     <num>          <int>     <num>         <num>         <int>
#>   1:   35.150  -116.260          10340        NA            NA             1
#>   2:   38.900  -110.190          10550        NA            NA             1
#>   3:   43.410  -104.470          10460        NA            NA             1
#>   4:   47.810   -97.360          10400        NA            NA             1
#>   5:   51.860   -89.110          10260        NA            NA             1
#>  ---                                                                        
#> 413:   30.093   139.568           7000  -999.999      -999.999             1
#> 414:   31.069   139.712           7600  -999.999      -999.999             1
#> 415:   31.870   139.856           7600  -999.999      -999.999             1
#> 416:   32.814   139.976           7500  -999.999      -999.999             1
#> 417:   33.746   140.312           5100  -999.999      -999.999             1
#>               local_time    lh
#>                   <POSc> <int>
#>   1: 2020-01-08 18:29:17    18
#>   2: 2020-01-08 19:37:34    19
#>   3: 2020-01-08 20:44:27    20
#>   4: 2020-01-08 21:56:53    21
#>   5: 2020-01-08 23:13:53    23
#>  ---                          
#> 413: 2020-12-10 14:06:36    14
#> 414: 2020-12-10 14:20:10    14
#> 415: 2020-12-10 14:30:45    14
#> 416: 2020-12-10 14:43:14    14
#> 417: 2020-12-10 14:55:34    14
```

## Receptors

Now we can do the last step which is generating the receptor list files.
Now we filter selected columns

```{r receptors, eval = F}
receptor <- master[, c("site_code",
                       "year",
                       "month",
                       "day",
                       "hour",
                       "minute",
                       "second",
                       "latitude",
                       "longitude",
                       "altitude_final",
                       "type_altitude")]

```

We can round altitude also

```{r round_alt, eval = F}
receptor$altitude_final <- round(receptor$altitude_final)
```

Now we can format time variables with two digits

```{r formatrec, eval = F}
receptor <- obs_format(receptor,
                        spf =  c("month", "day",
                                 "hour", "minute", "second"))
```

We have a column that indicate AGL or ASL

```{r aslagl, eval = F}
receptor_agl <- receptor[type_altitude == 0]
receptor_asl <- receptor[type_altitude == 1]
```

Finally, we save the receptors

```{r save_receptors, eval = F}
if(nrow(receptor_agl) > 0) {
  message(paste0(out, "_", datasetid, "_receptor_AGL.txt"), "\n")

  fwrite(x = receptor_agl,
         file = paste0(out, "_", datasetid, "_receptor_AGL.txt"),
         sep = " ")
}

if(nrow(receptor_asl) > 0) {
  message(paste0(out, "_", datasetid, "_receptor_ASL.txt"), "\n")

  fwrite(x = receptor_asl,
         file = paste0(out, "_", datasetid, "receptor_ASL.txt"),
         sep = " ")

}



```

```
#> C:\Users\sibarrae\AppData\Local\Temp\RtmpIP4sJV\file71b065447a30_aircraft-flask_receptor_ASL.txt
```

## Plot

Finally, we just plot some data, run it locally


```{r obs_plotsave, fig.width=5, fig.height=3, eval = F, echo = F, message=F, warning=F}
png("../man/figures/obsplot_aircraftflask.png", width = 1500, height = 1000, res = 200)
obs_plot(df3, time = "timeUTC", yfactor = 1e9)
dev.off()
```

```{r obs_plot, fig.width=7, fig.height=5, eval = F}
obs_plot(df3, time = "timeUTC", yfactor = 1e9)
```

```
#> Found the following sites: 
#> [1] IAGOS CON   AOA  
#> Plotting the following sites: 
#> [1] IAGOS CON
#> png 
#>   2
```

![Times Series](https://github.com/ibarraespinosa/rtorf/blob/main/man/figures/obsplot_aircraftflask.png?raw=true)


```{r savesf, fig.width=5, fig.height=3, eval = F, echo = F}
library(sf)
x <- st_as_sf(df3, coords = c("longitude", "latitude"), crs = 4326)
png("../man/figures/obsplot_aircraftflask_map.png", width = 1500, height = 1000, res = 200)
plot(x["value"], axes = T, reset = F)
maps::map(add = T)
dev.off()
```


```{r sf, fig.width=7, fig.height=5, eval = F}
library(sf)
x <- st_as_sf(df3, coords = c("longitude", "latitude"), crs = 4326)
plot(x["value"], axes = T, reset = F)
maps::map(add = T)
```

![Map](https://github.com/ibarraespinosa/rtorf/blob/main/man/figures/obsplot_aircraftflask_map.png?raw=true)



```{r vertplot, fig.width=5, fig.height=3, eval = F, echo = F, message=F, warning=F}
x <- df3
x$ch4 <- x$value*1e+9
png("../man/figures/obsplot_aircraftflask_vert.png", width = 1500, height = 1000, res = 200)
obs_plot(x, 
         time = "ch4", 
         y = "altitude_final", 
         colu = "month", n = c(1L, 3L, 6L, 8L, 9L, 11L, 12L), 
         type = "b", 
         xlab = expression(CH[4]~ppb), 
         ylab = "altitude (m)")
dev.off()
```

```{r obs_plot_vertt, fig.width=7, fig.height=5, eval = F}
x <- df3
x$ch4 <- x$value*1e+9
obs_plot(x, 
         time = "ch4", 
         y = "altitude_final", 
         colu = "month", #n = c(1L, 3L, 6L, 8L, 9L, 11L, 12L), 
         type = "b", 
         xlab = expression(CH[4]~ppb), 
         ylab = "altitude (m)")
```


![Vertical profiles](https://github.com/ibarraespinosa/rtorf/blob/main/man/figures/obsplot_aircraftflask_vert.png?raw=true)