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#just backup

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tinoheimhuber 7 years ago
parent d56262cd14
commit 6baeb9c237
4 changed files with 331 additions and 14 deletions
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35
Analysis/Code/P1_SILO_Batch_Download.py
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@ -20,14 +20,12 @@ from matplotlib.backends.backend_pdf import PdfPages
from ggplot import * from ggplot import *
matplotlib.style.use('ggplot') matplotlib.style.use('ggplot')
import csv import csv
from datetime import datetime
# import own modules # import own modules
# Set working direcotry (where postprocessed NARClIM data is located) # Set working direcotry (where postprocessed NARClIM data is located)
os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Analysis/Code') os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/Analysis/Code')
import climdata_fcts as fct #import climdata_fcts as fct
import silo as sil import silo as sil
#==========================================================#
#==========================================================# #==========================================================#
# Set working direcotry (where postprocessed NARClIM data is located) # Set working direcotry (where postprocessed NARClIM data is located)
@ -39,9 +37,13 @@ os.chdir('C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdo
#set input parameters #set input parameters
Case_Study_Name = 'CASESTUDY2' Case_Study_Name = 'CASESTUDY2'
Casestudy2_csv_path = "Data/NARCLIM_Site_CSVs/CASESTUDY2/CASESTDUY2_NARCLIM_Point_Sites.csv" Casestudy2_csv_path = "Data/NARCLIM_Site_CSVs/CASESTUDY2/CASESTDUY2_NARCLIM_Point_Sites.csv"
Silo_variables = ['daily_rain', "max_temp", "min_temp", 'et_short_crop'] Silo_variables = ['daily_rain', "max_temp", "min_temp", 'et_short_crop', 'evap_syn']
Location = 'Catchment'
startdate= '19600101'
enddate= '20180101'
#==========================================================# #==========================================================#
#==========================================================# #==========================================================#
#set directory path for output files #set directory path for output files
output_directory = 'Data/SILO/' + Case_Study_Name + '/' output_directory = 'Data/SILO/' + Case_Study_Name + '/'
@ -52,19 +54,25 @@ if not os.path.exists(output_directory):
print("output directory folder didn't exist and was generated") print("output directory folder didn't exist and was generated")
print('-------------------------------------------') print('-------------------------------------------')
#==========================================================# #==========================================================#
#==========================================================#
#read the CSV to extract the lat long and case stuy sites #read the CSV to extract the lat long and case stuy sites
with open(Casestudy2_csv_path, mode='r') as infile: with open(Casestudy2_csv_path, mode='r') as infile:
reader = csv.reader(infile) reader = csv.reader(infile)
next(reader, None) next(reader, None)
with open('coors_new.csv', mode='w') as outfile: with open('coors_new.csv', mode='w') as outfile:
writer = csv.writer(outfile) writer = csv.writer(outfile)
mydict = dict((rows[0],[rows[1],rows[2]]) for rows in reader) if Location == 'Estuary':
mydict = dict((rows[0],[rows[1],rows[2]]) for rows in reader)
if Location == 'Ocean':
mydict = dict((rows[0],[rows[3],rows[4]]) for rows in reader)
if Location == 'Catchment':
mydict = dict((rows[0],[rows[5],rows[6]]) for rows in reader)
for Estuary in mydict: for Estuary in mydict:
print Estuary print Estuary, mydict[Estuary][0], mydict[Estuary][1]
print str(mydict[Estuary][0])
#==========================================================# #==========================================================#
#set directory path for output files #set directory path for output files
@ -77,11 +85,10 @@ for Estuary in mydict:
print('-------------------------------------------') print('-------------------------------------------')
#==========================================================# #==========================================================#
output_csv = output_directory_internal + 'SILO_climdata_' + Estuary +'_' + mydict[Estuary][0] + '_' + mydict[Estuary][1] + '.csv' output_csv = output_directory_internal + 'SILO_climdata_' + Estuary +'_'+ Location +'_' + mydict[Estuary][0] + '_' + mydict[Estuary][1] + '2.csv'
silo_df = sil.pointdata(Silo_variables, 'Okl9EDxgS2uzjLWtVNIBM5YqwvVcCxOmpd3nCzJh','19900101','20100101', silo_df = sil.pointdata(Silo_variables, 'Okl9EDxgS2uzjLWtVNIBM5YqwvVcCxOmpd3nCzJh',startdate, enddate,
None, mydict[Estuary][0], mydict[Estuary][1], True, output_csv) None, mydict[Estuary][0], mydict[Estuary][1], True, output_csv)
#==========================================================#

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Analysis/Code/climdata_fcts.pyc
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Analysis/Code/ggplot_time_series_with_trends.R
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#R code for creating ggplots of time series with smooth (GAM) and linear term
######################
#Import Libraries and set working directory
######################
library(zoo)
library(hydroTSM) #you need to install these packages first before you can load them here
library(lubridate)
library(mgcv)
library(ggplot2)
library(gridExtra)
library(scales)
options(scipen=999)
setwd("C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/")
######################
######################
#Set inputs
######################
Case.Study <- "CASESTUDY2"
Estuary <- "HUNTER"
Climvar <- 'tasmean'
ggplotGAM.k <- 14
######################
######################
#Set input file paths
######################
AirT_CSV_Path <- "./Data/SILO/CASESTUDY2/HUNTER/SILO_climdata_HUNTER_Catchment_-32.162479_150.5335812.csv"
RivT_CSV_Path <- "./Data/River_Gauge_Data/HUNTER_210064_20180615/210064_Temp.csv"
SST_CSV_Path <- './Data/NARCLIM_Site_CSVs/CASESTUDY2/HUNTER_32.751_151.690/sstmean_NNRP_HUNTER_33.034_152.156_NARCliM_summary.csv'
######################
######################
#Analyse
######################
############tasmean
#Load a daily (no gaps) time series as a time serie baseline for other time series used here
AirT.df <- data.frame(read.csv(AirT_CSV_Path))
AirT.full.TS <- zoo((AirT.df$max_temp_Celsius + AirT.df$max_temp_Celsius)/2, order.by= as.Date(AirT.df[,"date"], format = "%Y-%m-%d")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
AirT.TS <- window(AirT.full.TS, start=as.Date("1990-01-01"), end=as.Date("2018-01-01"))
AirT.full.df <- data.frame(AirT.full.TS)
AirT.df <- data.frame(AirT.TS)
colnames(AirT.df) <- 'tasmean'
colnames(AirT.full.df) <- 'tasmean'
############
AirT.df$Julday1 <- seq(1,length(AirT.df[,1]),1)
linear.trend.model_EC_all <- lm(tasmean ~ Julday1, AirT.df)
AirT.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4]
AirT.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 356
############
AirT.full.df$Julday1 <- seq(1,length(AirT.full.df[,1]),1)
linear.trend.model_EC_all <- lm(tasmean ~ Julday1, AirT.full.df)
AirT.full.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4]
AirT.full.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 356
############tasmean
############River temp
#Load a daily (no gaps) time series as a time serie baseline for other time series used here
RivT.df <- data.frame(read.csv(RivT_CSV_Path))
RivT.full.TS <- zoo(RivT.df$Temp, order.by= as.Date(RivT.df[,"Date"], format = "%d/%m/%Y")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
RivT.TS <- window(RivT.full.TS, start=as.Date("1995-01-01"), end=as.Date("2018-01-01"))
RivT.full.df <- data.frame(RivT.TS) ### This is only done because
RivT.df <- data.frame(RivT.TS)
colnames(RivT.df) <- 'rivTmean'
colnames(RivT.full.df) <- 'rivTmean'
############
RivT.df$Julday1 <- seq(1,length(RivT.df[,1]),1)
linear.trend.model_EC_all <- lm(rivTmean ~ Julday1, RivT.df)
RivT.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4]
RivT.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 356
RivT.full.df$Julday1 <- seq(1,length(RivT.full.df[,1]),1)
linear.trend.model_EC_all <- lm(rivTmean ~ Julday1, RivT.full.df)
RivT.full.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4]
RivT.full.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 356
############River temp
############ SST
#Load a daily (no gaps) time series as a time serie baseline for other time series used here
SST.df <- data.frame(read.csv(SST_CSV_Path))
SST.full.TS <- zoo(SST.df$NNRP_R1_1950-273.15, order.by= as.Date(SST.df[,"X"], format = "%Y-%m-%d")) #=daily time series of rainfall for creation of clean, daily TS of ET and Q
SST.TS <- window(SST.full.TS, start=as.Date("1990-01-01"), end=as.Date("2018-01-01"))
SST.full.df <- data.frame(SST.full.TS)
SST.df <- data.frame(SST.TS)
str(SST.df)
colnames(SST.df) <- 'SSTmean'
colnames(SST.full.df) <- 'SSTmean'
############
SST.full.df$Julday1 <- seq(1,length(SST.full.df[,1]),1)
linear.trend.model_EC_all <- lm(SSTmean ~ Julday1, SST.full.df)
SST.full.pvalNCV_ECall <- summary(linear.trend.model_EC_all)$coefficients[2,4]
SST.full.lintrend <- summary(linear.trend.model_EC_all)$coefficients[2,1] * 356
SST.df$Julday1 <- seq(1,length(SST.df[,1]),1)
linear.trend.model_EC_all2 <- lm(SSTmean ~ Julday1, SST.df)
SST.pvalNCV_ECall <- summary(linear.trend.model_EC_all2)$coefficients[2,4]
SST.lintrend <- summary(linear.trend.model_EC_all2)$coefficients[2,1] * 356
############ SST
######################
#Plot
######################
##################################### Full Time Period
#Air temp Full period
p1air <- ggplot(AirT.full.df, aes(y=tasmean, x=index(AirT.full.TS))) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary, " - Linear and smooth trend in catchment airT (SILO) lin trend was ",
round(AirT.full.lintrend,3), ' C°/year with p=', round(AirT.full.pvalNCV_ECall,10), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("Air Temperature [C°]") + xlab("Time")
#Riv temp Full period
p1riv <- ggplot(RivT.full.df, aes(y=rivTmean, x=index(RivT.TS))) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary, " - Linear and smooth trend in river temperature (GAUGE) lin trend was ",
round(RivT.full.lintrend,3), ' C°/year with p=', round(RivT.full.pvalNCV_ECall,10), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("River Temperature [C°]") + xlab("Time")
#Sea temp Full period
p1sst <- ggplot(SST.full.df, aes(y=SSTmean, x=index(SST.full.TS))) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary, " - Linear and smooth trend in sea surface temperature (NNRP) lin trend was ",
round(SST.full.lintrend,3), ' C°/year with p=', round(SST.full.pvalNCV_ECall,10), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("Sea Surface Temperature [C°]") + xlab("Time")
#export to png
png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_tasmean_full_period_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1air,ncol=1)
dev.off()
png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_rivTmean_full_period_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1riv,ncol=1)
dev.off()
png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_SSTmean_full_period_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1sst,ncol=1)
dev.off()
##################################### Full Time Period
######################### 1990-present
combined.TS <- window(merge(AirT.full.TS, window(RivT.full.TS, start=as.Date("1995-01-01"), end=end(RivT.full.TS)), SST.full.TS, all=T), start=as.Date("1990-01-01"), end=end(AirT.full.TS))
combined.df <- data.frame(combined.TS)
colnames(combined.df) <- c('tasmean','rivTmean', 'SSTmean')
#Air temp
p1air <- ggplot(combined.df, aes(y=tasmean, x=index(combined.TS))) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary, " - Linear and smooth trend in catchment airT (SILO) lin trend was ",
round(AirT.lintrend,3), ' C°/year with p=', round(AirT.pvalNCV_ECall,10), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("Air Temperature [C°]") + xlab("Time")
#Riv temp
p1riv <- ggplot(combined.df, aes(y=rivTmean, x=index(combined.TS))) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary, " - Linear and smooth trend in river temperature (GAUGE) lin trend was ",
round(RivT.lintrend,3), ' C°/year with p=', round(RivT.pvalNCV_ECall,10), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("River Temperature [C°]") + xlab("Time")
#Sea temp
p1sst <- ggplot(combined.df, aes(y=SSTmean, x=index(combined.TS))) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary, " - Linear and smooth trend in sea surface temperature (NNRP) lin trend was ",
round(SST.lintrend,3), ' C°/year with p=', round(SST.pvalNCV_ECall,10), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
stat_smooth(method=gam, formula=y~s(x, k=ggplotGAM.k), se=T, size=0.5) +
ylab("Sea Surface Temperature [C°]") + xlab("Time")
#export to png
png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_tasmean_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1air,ncol=1)
dev.off()
png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_rivTmean_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1riv,ncol=1)
dev.off()
png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_SSTmean_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1sst,ncol=1)
dev.off()
#export an overview plot as png
gA <- ggplotGrob(p1air)
gB <- ggplotGrob(p1riv)
gC <- ggplotGrob(p1sst)
gA$widths <- gB$widths
gC$widths <- gB$widths
png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_Summary_1990-present_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(gA,gB ,gC,ncol=1)
dev.off()

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Analysis/Code/ggplot_time_series_with_trends_sea_levle.R
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#R code for creating ggplots of time series with smooth (GAM) and linear term
######################
#Import Libraries and set working directory
######################
library(zoo)
library(hydroTSM) #you need to install these packages first before you can load them here
library(lubridate)
library(mgcv)
library(ggplot2)
library(gridExtra)
library(scales)
options(scipen=999)
setwd("C:/Users/z5025317/OneDrive - UNSW/WRL_Postdoc_Manual_Backup/WRL_Postdoc/Projects/Paper#1/")
######################
######################
#Set inputs
######################
Case.Study <- "CASESTUDY2"
Estuary <- "HUNTER"
Climvar <- 'tasmean'
ggplotGAM.k <- 7
######################
######################
#Set input file paths
######################
AirT_CSV_Path <- "./Data/Ocean_Data/BOM_monthly_SL_Hunter_Newcastle.txt"
dat = readLines(AirT_CSV_Path)
dat = as.data.frame(do.call(rbind, strsplit(dat, split=" {2,10}")), stringsAsFactors=FALSE)
colnames(dat) <-dat[3,]
dat2 = dat[-c(1:3), ]
dat2 = dat2[-(720:726),]
dat2 = dat2[-(1:108),]
dat2 = dat2[,-1]
dat2$Date <- as.yearmon(dat2[,1], "%m %Y")
SeaLev.df <- dat2
head(SeaLev.df)
SeaLev.df$MSL <- as.numeric(SeaLev.df$Mean)
SeaLev.df$Julday1 <- seq(1,length(SeaLev.df[,1]),1)
linear.trend.model_EC_all <- lm(MSL ~ Julday1, SeaLev.df)
SeaLev.pvalNCV_ECall <- summary(linear.trend.model_EC_all )$coefficients[2,4]
SeaLev.lintrend <- summary(linear.trend.model_EC_all )$coefficients[2,1] * 12
######################
#Plot
######################
##################################### Full Time Period
p1air <- ggplot(SeaLev.df, aes(y=MSL, x=Date)) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary, " - Linear and smooth trend in monthly mean sea level (BOM Gauge) | lin trend was ",
round(100*SeaLev.lintrend,3), ' cm/year with p=', round(SeaLev.pvalNCV_ECall,10), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) +
geom_smooth(method='lm',fill="green", formula=y~x, colour="darkgreen", size = 0.5) +
stat_smooth(method=gam, formula=y~s(x, k=4), se=T, size=0.5, col="red") +
ylab("Monthly Mean Sea Level [m]") + xlab("Time")
#export to png
png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_MonthlyMeanSeaLevel_full_period_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1air,ncol=1)
dev.off()
#multiple smooths
p1air <- ggplot(SeaLev.df, aes(y=MSL, x=Date)) + geom_line(alpha=0.5) +
ggtitle(paste(Estuary, " - Linear and smooth trend in monthly mean sea level (BOM Gauge) | lin trend was ",
round(100*SeaLev.lintrend,3), ' cm/year with p=', round(SeaLev.pvalNCV_ECall,10), sep=" ")) +
theme(plot.title=element_text(face="bold", size=9)) +
stat_smooth(method=gam, formula=y~s(x, k=13), se=T, size=0.5, col="red") +
#stat_smooth(method=gam, formula=y~s(x, k=8), se=T, size=0.5, cor="blue") +
stat_smooth(method=gam, formula=y~s(x, k=5), se=T, size=0.5, col="green") +
ylab("Monthly Mean Sea Level [m]") + xlab("Time")
#export to png
png.name <- paste('./Output/', Case.Study, '/', Estuary, '/Trends_MonthlyMeanSeaLevel_MultiGAM_full_period_', Sys.Date(),".png", sep="")
png(file = png.name, width = 10.5, height = 7, units='in', res=500)
grid.arrange(p1air,ncol=1)
dev.off()
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