Package 'weaana' reference manual

Title:	Analysis the Weather Data
Description:	Functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. Demonstration data are included the SILO daily climate data (licensed under CC BY 4.0, <https://www.longpaddock.qld.gov.au/silo/>).
Authors:	Bangyou Zheng [aut, cre]
Maintainer:	Bangyou Zheng <[email protected]>
License:	MIT + file LICENSE
Version:	0.3.1
Built:	2026-05-21 08:50:17 UTC
Source:	https://github.com/byzheng/weaana

Getter to access the weather data at a specific position.

Description

Getter to access the weather data at a specific position.

Usage

## S4 method for signature 'WeaAna'
x[i, j, drop]
## S4 method for signature 'WeaAna'
x[i, j, drop]

Arguments

x

A WeaAna object.

i

the specific position which will access.

j

None use parameter.

drop

None use parameter.

Value

A WeaAnaSite object at the position i.

Examples

library(weaana)
data( "WeatherRecordsDemo" ) 
records[1]
records[1:2]
records[2:2]

library(weaana)
data( "WeatherRecordsDemo" ) 
records[1]
records[1:2]
records[2:2]

Add relative humidity to APSIM weather data

Description

This function adds relative humidity data to APSIM weather records based on a standard weather file.

Usage

add_rh(apsim, standard, output = NULL, overwrite = FALSE)
add_rh(apsim, standard, output = NULL, overwrite = FALSE)

Arguments

apsim

A character string specifying the path to the APSIM weather file.

standard

A character string specifying the path to the standard weather file containing relative humidity data.

output

A character string specifying the path to save the modified APSIM weather file with relative humidity data. If NULL, the function will not save the file.

overwrite

A logical value indicating whether to overwrite the output file if it already exists. Default is FALSE.

Value

A data frame containing the new weather records with relative humidity added.

Change weather records

Description

Change weather records

Usage

changeWeatherRecords(object, ...)

## S4 method for signature 'WeaAna'
changeWeatherRecords(object, ...)
changeWeatherRecords(object, ...)

## S4 method for signature 'WeaAna'
changeWeatherRecords(object, ...)

Arguments

object

A WeaAna object.

...

New weather records

Value

A new WeaAna object with updated records

Summarise the climate variable by growth stages

Description

Summarise the climate variable by growth stages

Usage

climate_by_stages(
  climates,
  sowing,
  emergence,
  heading = NULL,
  flowering = NULL,
  maturity,
  latitude
)
climate_by_stages(
  climates,
  sowing,
  emergence,
  heading = NULL,
  flowering = NULL,
  maturity,
  latitude
)

Arguments

climates

a data.frame for climate records

sowing

date. an vector of sowing date

emergence

numeric (days after sowing). an vector of emergence date

heading

numeric (days after sowing). an vector of heading date (optional. see details)

flowering

numeric (days after sowing). an vector of flowering time (optional. see details)

maturity

numeric (days after sowing). an vector of maturity time

latitude

latitude

Details

Define of growth stages

S0: From start of year to emergence
S1: From emergence to flowering time - 300Cd
S2: From flowering time - 300Cd to flowering time + 100Cd
S3: From flowering time + 100 Cd to flowering time + 600Cd
S4: From flowering + 600Cd to maturity

Climate variables

stage: defination of stages
n: Number of days in each stage
avgt: average temperature (C)
sum.tt: total thermal time (Cd) with base temperature 0C
avg.mint: average minimum temperature
avg.maxt: average maximum temperature
sum.rain: total rainfall
avg.evap: average evapration
avg.radn average radiation
hot.days: number of hot days (daily maximum temperature is more than 30C)
very.hot.days: number of very hot days (daily maximum temperature is more than 35C)
frost.days: number of frost days (daily minimum temperature is less than 0C)
hot.sum: total thermal time above 30C of hot days (daily maximum temperature is more than 30C)
very.hot.sum: total thermal time above 35C of very hot days (daily maximum temperature is more than 35C)
frost.sum: total thermal time below 0C of frost days (daily minimum temperature is less than 0C)
vpd: vapour-pressure deficit
te: transpiration efficiency
bio.radn: bio.radn
bio.water: bio.water
bio.tt: bio.tt
ptq: photothermal quotient
avt.diffuse.radn: average diffuse radiation

Value

a data.frame for summarised climate variable by stages. See details for more information.

Examples

if (FALSE) {
   sowing <- rep(as.Date("1981-05-01"), 10)
   emergence <- rep(10, 10)
   heading <- NULL
   flowering <- runif(10) * 20 + 50
   maturity <- runif(10) * 20 + 100
   latitude <- -27
   res <- climate_by_stages(climates = climates, 
                            sowing = sowing,
                            emergence = emergence, 
                            heading = heading,
                            flowering = flowering,
                            maturity = maturity,
                            latitude = latitude)
}
if (FALSE) {
   sowing <- rep(as.Date("1981-05-01"), 10)
   emergence <- rep(10, 10)
   heading <- NULL
   flowering <- runif(10) * 20 + 50
   maturity <- runif(10) * 20 + 100
   latitude <- -27
   res <- climate_by_stages(climates = climates, 
                            sowing = sowing,
                            emergence = emergence, 
                            heading = heading,
                            flowering = flowering,
                            maturity = maturity,
                            latitude = latitude)
}

Convert a data frame to weaana class

Description

Convert a data frame to weaana class

Usage

convert2Records(infor, records)
convert2Records(infor, records)

Arguments

infor

A list or data frame of site information

records

A data frame will convert to records

Value

A new WeaAna object

create WeaAna class

Description

create WeaAna class

Usage

createWeaAna(mets)
createWeaAna(mets)

Arguments

mets

A list contained information of weather records.

Value

A new WeaAna class

The time elapsed in hours between the specified sun angle from 90 degree in am and pm. +ve above the horizon, -ve below the horizon.

Description

The time elapsed in hours between the specified sun angle from 90 degree in am and pm. +ve above the horizon, -ve below the horizon.

Usage

dayLength(doy, lat, angle = -6)
dayLength(doy, lat, angle = -6)

Arguments

doy

day of year number

lat

latitude of site (deg)

angle

angle to measure time between, such as twilight (deg). angular distance between 90 deg and end of twilight - altitude of sun. +ve up, -ve down.

Value

day length in hours

Calculate the diurnal variation in air temperature with Parton and Logan, 1981

Description

Calculate the diurnal variation in air temperature. Parton WJ, Logan JA (1981) A model for diurnal variation in soil and air temperature. Agricultural Meteorology, 23, 205?216. Codes copied from APSIM Utilities.cpp

Usage

diurnalT(maxt, mint, doy, hour, latitude, A = 1.5, B = 4, C = 1)
diurnalT(maxt, mint, doy, hour, latitude, A = 1.5, B = 4, C = 1)

Arguments

maxt

maximum daily temperature

mint

minimum daily temperature

doy

day of year

hour

hour from 1 to 24

latitude

latitude in radials

A

is the time lag in temperature after noon

B

is coef that controls temperature decrease at night

C

is the time lag for min temperature after sunrise

Value

A vector with diurnal air temperature

Examples

diurnalT(maxt = 20, mint = 10, doy  = 1, 
   hour = seq(from = 1, to = 23.99, by = 0.1), 
   latitude = -10, A = 1.5, B = 4, C = 1)
diurnalT(maxt = 20, mint = 10, doy  = 1, 
   hour = seq(from = 1, to = 23.99, by = 0.1), 
   latitude = -10, A = 1.5, B = 4, C = 1)

Estimate Dew Point Temperature from Minimum Air Temperature and Relative Humidity

Description

Computes the dew point temperature (°C) using the Magnus–Tetens approximation, a widely used empirical relationship between air temperature, relative humidity, and the saturation vapor pressure of water vapor. The method provides a good approximation for typical atmospheric conditions and is suitable for most meteorological and agricultural applications.

Usage

dp_mint(mint, minrh)
dp_mint(mint, minrh)

Arguments

mint

Numeric. Minimum air temperature (°C).

minrh

Numeric. Relative humidity (%) corresponding to the minimum temperature.

Details

The dew point temperature is estimated using the Magnus–Tetens equation:

$\gamma = \frac{a \cdot T}{b + T} + \ln\left(\frac{RH}{100}\right)$

$T_{dew} = \frac{b \cdot \gamma}{a - \gamma}$

where $T$ is air temperature (°C), $RH$ is relative humidity (%), and the empirical constants are $a = 17.62$ and $b = 243.12$ (for water over a liquid surface). This formulation is based on improvements to the original Magnus equation proposed by Alduchov and Eskridge (1996).

For air temperatures below 0 °C, slightly different coefficients are sometimes used to account for ice surfaces, but the above constants are generally suitable for most temperature ranges encountered in agricultural or meteorological data.

Value

Numeric. Dew point temperature (°C).

References

Alduchov, O. A., & Eskridge, R. E. (1996). Improved Magnus Form Approximation of Saturation Vapor Pressure. Journal of Applied Meteorology, 35(4), 601–609. doi:10.1175/1520-0450(1996)035<0601:IMFAOS>2.0.CO;2

Examples

# Dew point for 10 °C and 80% RH
dp_mint(10, 80)

# Vectorized input
dp_mint(c(5, 10, 15), c(90, 70, 50))

# Dew point for 10 °C and 80% RH
dp_mint(10, 80)

# Vectorized input
dp_mint(c(5, 10, 15), c(90, 70, 50))

Get all weather records by year range

Description

Get all weather records by year range

Usage

getWeatherRecords(object, ...)

## S4 method for signature 'WeaAna'
getWeatherRecords(object, yrange = NULL, vars = "all", ...)
getWeatherRecords(object, ...)

## S4 method for signature 'WeaAna'
getWeatherRecords(object, yrange = NULL, vars = "all", ...)

Arguments

object

A WeaAna object.

...

Other arguments

yrange

Year range.

vars

Variable

Value

A data frame with all weather records

Examples

library(weaana)
data( "WeatherRecordsDemo" ) 
getWeatherRecords( records, yrange = c( 2008, 2009 ) )
getWeatherRecords( records, yrange = c( 2008, 2009 ), length = 10 )

library(weaana)
data( "WeatherRecordsDemo" ) 
getWeatherRecords( records, yrange = c( 2008, 2009 ) )
getWeatherRecords( records, yrange = c( 2008, 2009 ), length = 10 )

Return a y value from a linear interpolation function

Description

Return a y value from a linear interpolation function

Usage

interpolationFunction(x, y, values, split = "\\s+")
interpolationFunction(x, y, values, split = "\\s+")

Arguments

x

y

values

values

split

split

Value

The interpolated values

Calculate the moving values

Description

Calculate the moving values

Usage

mov(x, k = 10, shift = "centre", fun = "mean")
mov(x, k = 10, shift = "centre", fun = "mean")

Arguments

x

A vector to calculate moving values

k

The moving windows

shift

if shift = "centre", then values are shifted to centre. if shift = "begin", then values are at begin of period. if shift = "end", then values are at end of period. The default value (centre) will be used if shift is other value.

fun

The method to calculate moving values. Curruntly, only "mean", "max", "min", and "sum" are supported. A NULL will be returned for any other values

Value

The moving value of vector x at moving windows k. A NULL will be returned for any unsupported fun

Use Calculate the moving average. For compatibility only.

Description

Note that for n = odd, can average at central period. If n = even, must average at end of period and then shift values

Usage

mov.avg(x, k = 10, shift = "centre")
mov.avg(x, k = 10, shift = "centre")

Arguments

x

A vector to calculate moving average

k

The moving windows

shift

Value

The moving average of vector x at moving windows n

Calculate the moving maximum. For compatibility only.

Description

Calculate the moving maximum. For compatibility only.

Usage

mov.max(x, k, shift = "centre")
mov.max(x, k, shift = "centre")

Arguments

x

A vector to calculate moving maximum

k

The moving windows

shift

Value

The moving maximum of vector x at moving windows k

Calculate the moving minimum. For compatibility only.

Description

Calculate the moving minimum. For compatibility only.

Usage

mov.min(x, k, shift = "centre")
mov.min(x, k, shift = "centre")

Arguments

x

A vector to calculate moving minimum

k

The moving windows

shift

Value

The moving minimum of vector x at moving windows k

Calculate the moving sum. For compatibility only.

Description

Calculate the moving sum. For compatibility only.

Usage

mov.sum(x, k, shift = "centre")
mov.sum(x, k, shift = "centre")

Arguments

x

A vector to calculate moving sum

k

The moving windows

shift

Value

The moving sum of vector x at moving windows k

Read weather records from a file list and/or a folder list

Description

Read weather records from a file list and/or a folder list

Usage

readWeatherRecords(
  dataFiles = NULL,
  dataFolders = NULL,
  dataFormat = "APSIM",
  dataWeather = NULL,
  load.later = FALSE,
  ...
)
readWeatherRecords(
  dataFiles = NULL,
  dataFolders = NULL,
  dataFormat = "APSIM",
  dataWeather = NULL,
  load.later = FALSE,
  ...
)

Arguments

dataFiles

A character vector to specify the path of weather data files.

dataFolders

A character vector to specify the path of weather data folders.

dataFormat

The format of weather data file.

dataWeather

A data.frame for existing data.

load.later

Whether load weather records now or later. "dataFroamt" should be One of "APSIM" and "RDATA".

...

Other arguments

Value

A WeaAna class which contains all weather data.

Demo weather records

Description

Demo weather records

Usage

records
records

Format

An object of class WeaAna of length 1.

Define the class for statistics results

Description

Define the class for statistics results

Slots

name: Name of result
type: Type of result

Show basic information of class WeaAna

Description

Show the name, number, latitude, longitude of all weather stations.

Usage

## S4 method for signature 'WeaAna'
show(object)
## S4 method for signature 'WeaAna'
show(object)

Arguments

object

WeaAna objects

Examples

library(weaana)
data( "WeatherRecordsDemo" ) 
show( records )
records
library(weaana)
data( "WeatherRecordsDemo" ) 
show( records )
records

Get site information

Description

Get site information

Usage

siteInfor(object, ...)

## S4 method for signature 'WeaAna'
siteInfor(object, load.now = FALSE)

## S4 method for signature 'WeaAnaSite'
siteInfor(object, load.now = FALSE)
siteInfor(object, ...)

## S4 method for signature 'WeaAna'
siteInfor(object, load.now = FALSE)

## S4 method for signature 'WeaAnaSite'
siteInfor(object, load.now = FALSE)

Arguments

object

A WeaAnaSite object.

...

Not used

load.now

Whether load site information

Value

Site information in the WeaAna object

Site information in the WeaAnaSite object

Examples

library(weaana)
data( "WeatherRecordsDemo" ) 
siteInfor( records )
siteInfor( records, load.now = TRUE )
library(weaana)
data( "WeatherRecordsDemo" ) 
siteInfor( records )
siteInfor( records, load.now = TRUE )

Calculate the sphere distance

Description

Calculate the sphere distance

Usage

sphericalDistance(lat1, lon1, lat2, lon2)
sphericalDistance(lat1, lon1, lat2, lon2)

Arguments

lat1

Latitude

lon1

Longitude

lat2

Latitude

lon2

Longitude

Value

Distance in km

Calculate thermal time using cardinal temperatures

Description

Calculate thermal time using cardinal temperatures

Usage

thermalTime(weather, x_temp, y_temp, method = NULL)
thermalTime(weather, x_temp, y_temp, method = NULL)

Arguments

weather

WeaAna object

x_temp

The cardinal temperatures

y_temp

The effective thermal time

method

The method to calculate thermal time. The default method is ( maxt + mint ) / 2 - base. The three hour temperature methods will be used if method = '3hr'

Value

A data.frame with three columns: year, day and thermalTime.

Examples

met_file <- system.file("extdata/WeatherRecordsDemo1.met", package = "weaana")
records <- readWeatherRecords(met_file)
x_temp <- c(0, 26, 34)
y_temp <- c(0, 26, 0)
res <- thermalTime(records, x_temp, y_temp)
head(res)
res <- thermalTime(records, x_temp, y_temp, method = "3hr")
head(res)
met_file <- system.file("extdata/WeatherRecordsDemo1.met", package = "weaana")
records <- readWeatherRecords(met_file)
x_temp <- c(0, 26, 34)
y_temp <- c(0, 26, 0)
res <- thermalTime(records, x_temp, y_temp)
head(res)
res <- thermalTime(records, x_temp, y_temp, method = "3hr")
head(res)

Calculate thermal time using cardinal temperatures

Description

Calculate thermal time using cardinal temperatures

Usage

thermalTimeDaily(mint, maxt, x_temp, y_temp, method = NULL)
thermalTimeDaily(mint, maxt, x_temp, y_temp, method = NULL)

Arguments

mint

The minimum temperature

maxt

The maximum temperature

x_temp

The cardinal temperatures

y_temp

The effective thermal time

method

The method to calculate thermal time. The default method is ( maxt + mint ) / 2 - base. The three hour temperature methods will be usesd if method = '3hr'

Value

The thermal time.

Examples

mint <- c(0, 10)
maxt <- c(30, 40)
x_temp <- c(0, 20, 35)
y_temp <- c(0, 20, 0)
thermalTimeDaily(mint, maxt, x_temp, y_temp)
thermalTimeDaily(mint, maxt, x_temp, y_temp, method = '3hr')
mint <- c(0, 10)
maxt <- c(30, 40)
x_temp <- c(0, 20, 35)
y_temp <- c(0, 20, 0)
thermalTimeDaily(mint, maxt, x_temp, y_temp)
thermalTimeDaily(mint, maxt, x_temp, y_temp, method = '3hr')

Calculate thermal time using the hourly temperature (non daily temperature)

Description

Calculate thermal time using the hourly temperature (non daily temperature)

Usage

thermalTimeHourly(timestamp, temperature, x_temp, y_temp)
thermalTimeHourly(timestamp, temperature, x_temp, y_temp)

Arguments

timestamp

The timestamp of weather records

temperature

The temperature

x_temp

The cardinal temperatures

y_temp

The effective thermal time

Value

A data frame with daily thermal time

Examples

met_file <- system.file("extdata/WeatherHourly.csv", package = "weaana")
hourly <- read.csv(met_file, as.is = TRUE) 

hourly$timestamp <- as.POSIXct(hourly$timestamp, format = "%Y-%m-%dT%H:%M:%SZ")
x_temp <- c(0, 20, 35)
y_temp <- c(0, 20, 0)
thermalTimeHourly(hourly$timestamp, hourly$temperature, x_temp, y_temp)
met_file <- system.file("extdata/WeatherHourly.csv", package = "weaana")
hourly <- read.csv(met_file, as.is = TRUE) 

hourly$timestamp <- as.POSIXct(hourly$timestamp, format = "%Y-%m-%dT%H:%M:%SZ")
x_temp <- c(0, 20, 35)
y_temp <- c(0, 20, 0)
thermalTimeHourly(hourly$timestamp, hourly$temperature, x_temp, y_temp)

Hourly Air Temperature Interpolation

Description

Computes hourly estimates of air temperature (Ta) from daily Tmin, Tmax, and day length using the method of Goudriaan (1994). The function assumes a sinusoidal temperature variation during daylight hours and an exponential decay after sunset.

Usage

tt_hourly_pp(tmin, tmax, tmax_b, tmin_a, lat, doy)
tt_hourly_pp(tmin, tmax, tmax_b, tmin_a, lat, doy)

Arguments

tmin

Numeric. Minimum temperature of the day (°C).

tmax

Numeric. Maximum temperature of the day (°C).

tmax_b

Numeric. Maximum temperature from the previous day (°C).

tmin_a

Numeric. Minimum temperature of the following day (°C).

lat

Numeric. Latitude of the location (degrees, -90 to 90).

doy

Integer. Day of the year (1 to 366).

Details

- **Daytime interpolation**: During sunlight hours, Ta is estimated using a sinusoidal curve fitted between Tmin and Tmax. - **Nighttime interpolation**: After sunset, Ta follows an exponential decline from the sunset temperature to the next morning's Tmin. - **Sunrise and Sunset Calculation**: - Sunrise hour: 'hsrise = 12 - d/2' - Sunset hour: 'hsset = 12 + d/2' - Tmax occurs at hour = 13.5.

If the Controlled Environment module is used for weather data, the function takes hourly values from that instead of computing them.

Value

A numeric vector of length 24 containing the estimated hourly air temperatures (°C).

References

Goudriaan, J. (1994). Advanced Methods for Calculating Radiation Models. Netherlands Journal of Agricultural Science, 42(4), 315-323.

Examples

tmin <- 10
tmax <- 20
tmax_b <- 21
tmin_a <- 11
lat <- -27
doy <- 10
hourly_temps <- tt_hourly_pp(tmin, tmax, tmax_b, tmin_a, lat, doy)

tmin <- 10
tmax <- 20
tmax_b <- 21
tmin_a <- 11
lat <- -27
doy <- 10
hourly_temps <- tt_hourly_pp(tmin, tmax, tmax_b, tmin_a, lat, doy)

Significantly t-test with auto-correlation for time serial data

Description

Method is presented by Santer et al. 2000

Usage

ttest_ts(y, slope = NULL)
ttest_ts(y, slope = NULL)

Arguments

y

A vector of time serial data

slope

Whether export slope

Value

p values of t-test

Calculate weather variables through function or a string formula.

Description

There are two modes to use wcal, function mode if FUN is not null, and string formula mode if FUN is NULL.

Usage

wcal(object, ...)

## S4 method for signature 'WeaAna'
wcal(object, FUN = NULL, ..., var.args = NULL, var.name = NULL)
wcal(object, ...)

## S4 method for signature 'WeaAna'
wcal(object, FUN = NULL, ..., var.args = NULL, var.name = NULL)

Arguments

object

A WeaAna objects.

...

Optional arguments to FUN in function mode. String formulas if FUN is NULL.

FUN

A function to be used which results should have the same length as original records.

var.args

Arguments of weather variable pass to FUN.

var.name

Variable name is used if FUN is not NULL.

Examples

library(weaana)
data( "records" ) 
# Daily mean temperature
wcal( records, avgt2 = "( maxt + mint ) / 2" )
# Moving average temperature
wcal( records, FUN = mov.avg, var.args = "avgt", k = 5, shift = "begin", var.name = "mov.avg" )
library(weaana)
data( "records" ) 
# Daily mean temperature
wcal( records, avgt2 = "( maxt + mint ) / 2" )
# Moving average temperature
wcal( records, FUN = mov.avg, var.args = "avgt", k = 5, shift = "begin", var.name = "mov.avg" )

Define the class for multiple sites

Description

Define the class for multiple sites

Slots

num: total number of weather station
records: A pointer vector to weather records of each site
result: A pointer for all results name and type.

Define the class of WeaAna

Description

Define the class of WeaAna

Slots

name: Name of weather station
number: Station number of weather station
latitude: Latitude of weather station
longitude: Latitude of weather station
tav: Annual average ambient temperature
amp: Annual amplitude in mean monthly temperature
marker: The extra marker for this site
year: A vector of year of weather station
day: A vector of day of weather station
radn: A vector of radiation of weather station
maxt: A vector of maximum temperature of weather station
mint: A vector of minimum temperature of weather station
evap: A vector of evaporation of weather station
rain: A vector of rainfall of weather station
vp: A vector of pressure atmosphere of weather station
code: The 6 digit code indicates the source of the 6 data columns
extra: A list of variables need to store
res: All statistics results store in this slot
figures: A list to store all plotted figures.
file.path: The file path for this site.
data.format: The data format for this site.
load.later: Whether are records loaded laterly.

Write weather records into file

Description

Write weather records into file

Usage

writeWeatherRecords(object, ...)

## S4 method for signature 'WeaAna'
writeWeatherRecords(object, file, cols = NULL)
writeWeatherRecords(object, ...)

## S4 method for signature 'WeaAna'
writeWeatherRecords(object, file, cols = NULL)

Arguments

object

A WeaAna object.

...

Not used

file

Path of output file.

cols

Columns to export. All columns exported if NULL

Value

No return values

Package 'weaana'

Help Index

Getter to access the weather data at a specific position.

Description

Usage

Arguments

Value

Examples

Add relative humidity to APSIM weather data

Description

Usage

Arguments

Value

Change weather records

Description

Usage

Arguments

Value

Summarise the climate variable by growth stages

Description

Usage

Arguments

Details

Value

Examples

Convert a data frame to weaana class

Description

Usage

Arguments

Value

create WeaAna class

Description

Usage

Arguments

Value

The time elapsed in hours between the specified sun angle from 90 degree in am and pm. +ve above the horizon, -ve below the horizon.

Description

Usage

Arguments

Value

Calculate the diurnal variation in air temperature with Parton and Logan, 1981

Description

Usage

Arguments

Value

Examples

Estimate Dew Point Temperature from Minimum Air Temperature and Relative Humidity

Description

Usage

Arguments

Details

Value

References

Examples

Get all weather records by year range

Description

Usage

Arguments

Value

Examples

Return a y value from a linear interpolation function

Description

Usage

Arguments

Value

Calculate the moving values

Description

Usage

Arguments

Value

Use Calculate the moving average. For compatibility only.

Description

Usage

Arguments

Value

Calculate the moving maximum. For compatibility only.

Description

Usage

Arguments

Value