deployment.Rmd

---
title: "Deployment Planning"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
library(reticulate)
library(leaflet)
library(emoji)
# use_condaenv("py39")
```

This page is used for Canadian Argo groups to coordinate deployment planning, in addition to [OceanOps](https://www.ocean-ops.org/board?t=argo). To submit plans to the website, see instructions on the [github page](https://github.com/ArgoCanada/blog/tree/master/deployment).

The map below shows completed and planned deployments for 2025, coloured by institution. Dots with a buoy symbol are locations of completed deployments (deployment location, not most recent profile location), while dots with the loading symbol are planned deployments.

```{python data}
import pandas as pd

df = pd.read_csv('deployment/canada_deployments.csv')
df['DEPLOYMENT DATE'] = df['DEPLOYMENT DATE'].apply(pd.Timestamp)
df = df.loc[df['DEPLOYMENT DATE'] > pd.Timestamp('01-01-2025')]
df = df.sort_values('DEPLOYMENT DATE')
df['DEPLOYMENT DATE'] = [d.strftime('%d %b, %Y') for d in df['DEPLOYMENT DATE']]
df['REF'] = [str(int(w)) if pd.notna(w) else '' for w in df.REF]
df['IMEI'] = [str(int(i)) if pd.notna(i) else '' for i in df.IMEI]
df['SERIAL NUMBER'] = [s if pd.notna(s) else '' for s in df['SERIAL NUMBER']]
df['DEPLOYMENT SHIP'] = [s if pd.notna(s) else '' for s in df['DEPLOYMENT SHIP']]

mapper = {
  'PROGRAM':'Program',
  'INSTITUTE':'Institute',
  'STATUS':'Status',
  'MODEL':'Model',
  'MODEL_DETAIL':'Detail',
  'DEPLOYMENT DATE':'Date',
  'DEPLOYMENT LAT':'Latitude',
  'DEPLOYMENT LON':'Longitude',
  'DEPLOYMENT SHIP':'Ship',
  'IMEI':'IMEI',
  'REF':'WMO',
  'SERIAL NUMBER':'Serial No.',
}

df = df.rename(columns=mapper)
df = df[mapper.values()]
df = df.reset_index().drop('index', axis=1)

mdf = pd.read_csv("deployment/ship_info.csv")

recent = df.loc[df.Status == 'OPERATIONAL'].drop('Status', axis=1).reset_index().drop('index', axis=1)
recent_table = recent.drop(['IMEI', 'Model'], axis=1)
plan   = df.loc[df.Status == 'CONFIRMED'].drop('Status', axis=1).reset_index().drop('index', axis=1)
plan_table = plan.drop(['IMEI', 'Model'], axis=1)

n_floats = recent.shape[0]

model_mapper = {
  'ARVOR_SBE':'ARVOR-SBE',
  'ARVOR+DO':'ARVOR-SBE with Oxygen',
  'ARVOR_RBR':'ARVOR-RBR',
  'ARVOR_D':'Deep ARVOR with Oxygen',
  'PROVOR_CTS4':'PROVOR CTS4',
  'PROVOR_CTS4+pH':'PROVOR CTS4 with pH',
  'PROVOR_CTS5':'PROVOR CTS5',
  'PROVOR_CTS5+pH':'PROVOR CTS5 with pH',
  'PROVOR_CTS4+SUNA':'PROVOR CTS4 with Nitrate',
  'PROVOR_CTS5+SUNA':'PROVOR CTS5 with Nitrate',
  'APEX_SBE':'APEX-SBE',
  'APEX_RBR':'APEX-RBR',
}

if n_floats == 0:
  summary_string = 'So far in 2025 we have not deployed any floats.'
else:
  summary_string = f'To date we have deployed {n_floats} floats in 2025: '
  for flt in recent.Detail.unique():
    n = recent.loc[recent.Detail == flt].shape[0]
    if n > 0:
      summary_string += f'{n} {model_mapper[flt]}, '
  summary_string = summary_string[:-2]
  summary_string += '.'

```

```{r map, layout="l-page-outset"}
pal <- colorFactor(
  c("green", "purple", "blue", "yellow", "green"),
  levels = c("BIO", "IOS", "ONC", "Dal", "SAEON")
)
icons <- iconList(
  OPERATIONAL = makeIcon("images/buoy.png", "images/buoy.png", 16, 16),
  CONFIRMED = makeIcon("images/loading.png", "images/loading.png", 16, 16),
  FAILED = makeIcon("images/x.png", "images/x.png", 16, 16)
)

leaflet() %>%
  addTiles() %>%
    setView(lng = -60, lat = 45, zoom = 2) %>%
    addCircleMarkers(
      data = py$df,
      lat = py$df$Latitude,
      lng = py$df$Longitude,
      radius = 12,
      stroke = FALSE,
      fillColor = ~pal(py$df$Institute),
      fillOpacity = 0.7,
      popup = paste0(
        "WMO: ", py$df$WMO,
        "<br/>",
        "Model: ", py$df$Detail,
        "<br/>",
        "Program: ", py$df$Program, ", ", py$df$Institute,
        "<br/>",
        "Deployment Date: ", py$df$Date,
        "<br/>",
        "Ship: ", py$df$Ship
      )
    ) %>%
    addMarkers(
      data = py$df,
      lat = py$df$Latitude,
      lng = py$df$Longitude,
      icon = ~icons[py$df$Status],
      popup = paste0(
        "WMO: ", py$df$WMO,
        "<br/>",
        "Model: ", py$df$Detail,
        "<br/>",
        "Program: ", py$df$Program, ", ", py$df$Institute,
        "<br/>",
        "Deployment Date: ", py$df$Date,
        "<br/>",
        "Ship: ", py$df$Ship
      )
    )
```

## Deployments since Jan 1, 2025

`r py$summary_string`

```{r table_1, layout="l-page-outset shaded"}
assign_country <- function(name) {
  cty <- py$mdf$Country[py$mdf$Ship == name]
  paste(emoji::emoji_name[paste0("flag_", cty)], name)
}

link_efm <- function(wmo) {
  paste0("[",wmo,"](https://fleetmonitoring.euro-argo.eu/float/",wmo,")")
}

py$recent_table$Ship <- lapply(py$recent_table$Ship, assign_country)
py$recent_table$WMO <- lapply(py$recent_table$WMO, link_efm)
knitr::kable(py$recent_table)
```
## Planned Deployments

```{r table_2, layout="l-page-outset shaded"}
py$plan_table$Ship <- lapply(py$plan_table$Ship, assign_country)
knitr::kable(py$plan_table)
```

_Note_: The two PROVOR floats planned for deplpyment on line P from the Tully are awaiting replacement radiometry sensors as they were originally shipped with incorrect wavelength configurations, so those deployments should be regarded as tentative. 

## Planning & Inventory

```{python inventory}
stock = pd.read_csv('deployment/inventory.csv')

remain = pd.DataFrame({
  'Program':stock.Program,
  'Institute':stock.Institute,
  'ARVOR-SBE':stock['ARVOR-SBE'],
  'ARVOR+DO':stock['ARVOR+DO'],
  'ARVOR-RBR':stock['ARVOR-RBR'],
  'Deep ARVOR':stock['Deep ARVOR'],
  'PROVOR':stock['PROVOR'],
  'APEX-SBE':stock['APEX-SBE'],
  'APEX-RBR':stock['APEX-RBR']
})

remain = remain.set_index(['Program', 'Institute'], drop=False)

model_mapper = {
  'ARVOR_SBE':'ARVOR-SBE',
  'ARVOR+DO':'ARVOR+DO',
  'ARVOR_RBR':'ARVOR-RBR',
  'ARVOR_D':'Deep ARVOR',
  'PROVOR_CTS4':'PROVOR',
  'PROVOR_CTS4+pH':'PROVOR',
  'PROVOR_CTS5':'PROVOR',
  'PROVOR_CTS5+pH':'PROVOR',
  'PROVOR_CTS4+SUNA':'PROVOR',
  'PROVOR_CTS5+SUNA':'PROVOR',
  'APEX_SBE':'APEX-SBE',
  'APEX_RBR':'APEX-RBR',
}

for i in plan.index:
    ix = (plan.loc[i].Program, plan.loc[i].Institute)
    f = plan.loc[i].Detail
    remain.loc[ix, model_mapper[f]] -= 1
```

The table below shows floats "in stock" at various institutions.

```{r table_3, layout="l-body-outset shaded"}
knitr::kable(py$stock)
```

### Planned Missions

The following are planned missions that floats could be deployed on.

<!-- This table shows what the standing stock of floats will be for each program/institution will be following the completion of the deployments listed in the "Planned Deployments" section. 

```{r table_4, layout="l-body-outset shaded"}
knitr::kable(py$remain)
```
  -->