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library(pumpingtest)
D <- read.csv("Cuche.csv", h = T)
ptest <- pumping_test("Cuche", Q = .00349, r = 0.3, t = D$t, s = D$s)
additional.parameters(ptest) <- list(B=20)
ptest.fit <- fit(ptest,"hantush_jacob")

Error in model.frame.default(formula = ~s + ptest + t, data = drawdown_curve) : 
  invalid type (list) for variable 'ptest'

ptest.fit <- fit(ptest, "warren_root")
Error: warren_root_well_function: sigma is less than 0

rm(list=ls())

data("warren_root")

ptest <- pumping_test("Warren_root", Q = 3.58e-2, r = 0.11, t = warren_root$t, s = warren_root$s)

p1 <- plot(ptest)
## Joining, by = c("t", "s", "variable")
print(p1)



# Diagnostic Plots
# The diagnostic plot is the default option in the plot function, 
# in which the derivative of the drawdown with respect to the logarithm of time is calculated via central differences. 
# The drawdown derivative is very sensitive to the noise present in the drawdown measurements and therefore it is advisable to create the diagnostic plot using different derivative types. 
# In this case, four types of derivatives are used in the diagnostic plots:

p.central <- plot(ptest, dmethod = "central") +
  theme(legend.position="bottom")
p.horner <- plot(ptest, dmethod = "horner") +
  theme(legend.position="bottom")
p.bourdet <- plot(ptest, dmethod = "bourdet", d = 2) +
  theme(legend.position="bottom")
p.spline <- plot(ptest, dmethod = "spline", d = 20) +
  theme(legend.position="bottom")
## Joining, by = c("t", "s", "variable")
p.diagnostic <- grid.arrange(p.central, p.horner, p.bourdet, p.spline, 
                             nrow = 2)
print(p.diagnostic)

ptest <- na.omit(ptest)

###Parameter estimation### 
ptest.fit <- fit(ptest, "warren_root")
ptest.fit$parameters
ptest.fit$hydraulic_parameters

#Parameterise the estimated Parameters
Tr <- ptest.fit$hydraulic_parameters$Tr
Ss <- ptest.fit$hydraulic_parameters$Ss
Ri <- ptest.fit$hydraulic_parameters$radius_influence

hydraulic.parameters(ptest) <- ptest.fit$hydraulic_parameters
fit.parameters(ptest) <- ptest.fit$parameters
model(ptest) <- "cooper_jacob"
estimated(ptest) <- TRUE
p.estimation <- plot(ptest, type = "estimation", dmethod = "spline", 
                     d = 30) 
## Joining, by = c("t", "s", "variable")
print(p.estimation)

p.mod.diag <- plot(ptest, type = 'model.diagnostic')
## Joining, by = c("t", "s", "variable")
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'