---
title: "Using a manually downloaded CIMIS CSV with TrackTrap"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Using a manually downloaded CIMIS CSV with TrackTrap}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

## When to use this

Use `weather_source = "cimis_csv"` when you want ground truth station data instead
 of a gridded product (Daymet/Open-Meteo), or when you are working offline with 
pre-defined weather data.

## 1. Download the report

At the [CIMIS website](https://cimis.water.ca.gov): **Reports** > select
your station > report type **Daily** > units **English (°F)** > export as
CSV. The file will include `Date`, `Max Air Temp (F)`, and `Min Air Temp (F)`.

## 2. Know how `read.csv()` renames columns

`read.csv()` censors headers: `Max Air Temp (F)` becomes
`Max.Air.Temp..F.`, and `Min Air Temp (F)` becomes `Min.Air.Temp..F.` —
exactly the names `calc_pest_phenology()` expects.

```{r, eval = FALSE}
names(read.csv("my_station_daily.csv"))
#> [1] "Date" "Max.Air.Temp..F." "Min.Air.Temp..F." ...
```

## 3. Confirm the date format

CIMIS exports dates as `MM/DD/YYYY`. 

## 4. Run `calc_pest_phenology()`

Set `weather_source = "cimis_csv"` exactly (not `"cimis"`). `lat`/`lon` are
ignored in this mode since the station location is already implied in
the file.

```{r, eval = FALSE}
library(TrackTrap)

trap_df <- data.frame(
  date = as.Date(c("2024-04-01", "2024-04-08", "2024-04-15", "2024-04-22")),
  trap_counts = c(0, 0, 3, 9)
)

result <- calc_pest_phenology(
  trap_df, pest = "OLFF",
  weather_source = "cimis_csv",
  cimis_csv_path = "my_station_daily.csv"
)
```

## 5. Plot it

```{r, eval = FALSE}
plot_phenology_trend(result)
```