short_timescale_facet_plot = plot_multiple_timespecs(ral_data, ultrashort_times, stack_facets = T,
ground_state = ground_state, caption = experiment_caption,
time_units = "picoseconds", sep_windows = sep_windows)
ggsave(filename = paste0(ral_data_root_dir, "/processed/", name, "/", name, "_multispec.png"), plot = multispec_overlay)
ggsave(filename = paste0(ral_data_root_dir, "/processed/", name, "/", name, "_short_time_stacked.png"), plot = stacked_facet_plot,
width = 2434, height = 4868, units = "px", dpi = 600)
ggsave(filename = paste0(ral_data_root_dir, "/processed/", name, "/", name, "_long_timestacked.png"), plot = long_timescale_facet_plot,
width = 2434, height = 4868, units = "px", dpi = 600)
ggsave(filename = paste0(ral_data_root_dir, "/processed/", name, "/", name, "_ultrashort_timestacked.png"), plot = short_timescale_facet_plot,
width = 2434, height = 4868, units = "px", dpi = 600)
write_csv(ral_data, file= paste0(ral_data_root_dir, "/processed/", name, "/", name, "_processed.csv"))
}
produce_time_plots(jmlral932_proc, name = "JMLRAL932", ground_state = coco_pypy_ground, experiment_caption = "CoPyPy Breathing")
jmlral932_proc$Window = "L"
produce_time_plots(jmlral932_proc, name = "JMLRAL932", ground_state = coco_pypy_ground, experiment_caption = "CoPyPy Breathing")
library(cowplot)
produce_time_plots(jmlral932_proc, name = "JMLRAL932", ground_state = coco_pypy_ground, experiment_caption = "CoPyPy Breathing")
warnings()
#exp_descriptions = read_csv("Ral_experiment_descriptions.csv")
ral_data_root_dir = "E:/RAL_data/"
produce_time_plots(jmlral932_proc, name = "JMLRAL932", ground_state = coco_pypy_ground, experiment_caption = "CoPyPy Breathing")
get_peak_kinetics(jmlral932_proc, 2066, time_cutoff = 1, mode = "growth")
get_peak_kinetics(jmlral932_proc, 2066, time_cutoff = 0.5, mode = "growth")
get_peak_kinetics(jmlral932_proc, 2066, time_cutoff = 0.5, mode = "growth") -> copyp
copyp$plot -> coplot
coplot+theme_pubr(base_size = 16, base_family = "Arial")+theme(plot.title = element_blank(), plot.caption = element_blank)
coplot+theme_pubr(base_size = 16, base_family = "Arial")+theme(plot.title = element_blank(), plot.caption = element_blank())
coplot
jmlral926 = read_csv("E:/RAL_data/jmlral936_ubl.csv")
jmlral936 = jmlral926
rm(jmlral926)
mco8 = mco8_calib %>% rename("pixel" = Window, "Wavenumber"= wn)
jmlral936 %>% drop_duplicated_cols() %>% bind_rows(mco8) -> jmlral936_proc
plot_spectrum_at_time(jmlral936_proc, 0.5)
jmlral936
jmlral936_proc
plot_spectrum_at_time(jmlral936_proc, 0.25)
jmlral936_proc$Wavenumber
jmlral936
jmlral936$Wavenumber = mco8$Wavenumber
jmlral936$Wavenumber
jmlral936$Wavenumber %>% drop_duplicated_cols() ->jmlral936_proc
jmlral936 %>% drop_duplicated_cols() ->jmlral936_proc
plot_spectrum_at_time(jmlral936_proc, 0.25)
plot_spectrum_at_time(jmlral936_proc, 0.05)
plot_spectrum_at_time(jmlral936_proc, 0.005)
plot_spectrum_at_time(jmlral936_proc, 0.001)
plot_spectrum_at_time(jmlral936_proc, 0.0001)
plot_spectrum_at_time(jmlral936_proc, 0.0005)
plot_spectrum_at_time(jmlral936_proc, 0.0007)
plot_spectrum_at_time(jmlral936_proc, 0.003)
plot_spectrum_at_time(jmlral936_proc, 0.003) %>% ggplotly()
jmlral936_proc$Window = "L"
get_peak_kinetics(jmlral936_proc, 2061, time_cutoff=0.5, mode = "growth")
jmlral1236_proc = read_csv("E:/RAL_data/JMLRAL1236/JMLRAL1236_processed.csv")
plot_spectrum_at_time(jmlral1236_proc, 0.2)
plot_spectrum_at_time(jmlral1236_proc, 0.1)
plot_spectrum_at_time(jmlral1236_proc, 0.05)
plot_spectrum_at_time(jmlral1236_proc, 0.02)
plot_spectrum_at_time(jmlral1236_proc, 0.02) %>% ggplotly()
get_peak_kinetics(jmlral1236_proc, 2010, 0.5, mode= "growth")
plot_spectrum_at_time(jmlral1236_proc, 0.02) %>% ggplotly()
get_peak_kinetics(jmlral1236_proc, 1946, 0.5, mode= "growth")
plot_spectrum_at_time(jmlral1236_proc, 0.02) %>% ggplotly()
jmlral1230_proc = read_csv("E/RAL_data/JMLRAL1230/JMLRAL1230_processed.csv")
jmlral1230_proc = read_csv("E:/RAL_data/JMLRAL1230/JMLRAL1230_processed.csv")
get_peak_kinetics(jmlral1230_proc, 1925)
get_peak_kinetics(jmlral1230_proc, 1925, time_cutoff = 0.5, mode = "growth")
library(tidyverse)
library(ggpubr)
library(broom)
library(modelr)
library(cowplot)
source("C:/Users/TT-PC/Google Drive/ral_tddft/IR/IR_simulation.R")
source("C:/Users/TT-PC/Google Drive/ral_tddft/IR/IR_simulation.R")
coco_phccpyrone_simpath = "C:/Users/TT-PC/Google Drive/ral_tddft/IR/co_phccyprone_vib_bp86.txt"
generate_sim_ir(coco_phccpyrone_simpath)
generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), wavenumbers=c(400,3000))
generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), Wavenumbers=c(400,3000))
coco_phccpyrone_simpath = "C:/Users/TT-PC/Google Drive/ral_tddft/IR/co_phccpyrone_vib_bp86.txt"
generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), Wavenumbers=c(400,3000))
coco_phccpyrone_simpath = "C:/Users/TT-PC/Google Drive/ral_tddft/IR/co_phccpyrone_vib_bp86svp.txt"
generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), Wavenumbers=c(400,3000))
generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), Wavenumbers=c(400,3000)) %>% ggplot(aes(Wavenumber, additive))+geom_line(())
co_phpy = generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), Wavenumbers=c(400,3000)) %>% ggplot(aes(Wavenumber, additive))+geom_line()
co_phpy
co_phpy = generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), Wavenumbers=c(400,3000))
co_phpy
co_phpy$additive
co_phpy = generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), Wavenumbers=seq((400,3000)))
co_phpy = generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), Wavenumbers=seq(400,3000))
co_phpy %>% ggplot(aes(Wavenumber,additive))+geom_line(())
co_phpy %>% ggplot(aes(Wavenumber,additive))+geom_line()
co_phpy %>% ggplot(aes(Wavenumber,additive))+geom_line()+scale_x_reverse()
read_vibspec(coco_phccpyrone_simpath)
co_phpy = generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), peakwidth = 5, Wavenumbers=c(400,3000)) %>% ggplot(aes(Wavenumber, additive))+geom_line()
co_phpy
co_phpy = generate_sim_ir(read_vibspec(coco_phccpyrone_simpath), peakwidth = 5, Wavenumbers=seq(400,3000)) %>% ggplot(aes(Wavenumber, additive))+geom_line()+scale_x_reverse()
co_phpy
library(plotly)
ggplotly(co_phpy)
ir_calibration_data = read_csv("C:/Users/TT-PC/Google Drive/ral_tddft/IR/sim_ir_comparison.csv")
ir_calibration_data %>% ggplot(aes(Simulated,Experimental))+geom_point()
ir_calibration_data %>% ggplot(aes(Simulated,Experimental))+geom_point(aes(colour = Complex))
theme_set(theme_pubr(base_size = 16, base_family = "Arial"))
ir_calibration_data %>% ggplot(aes(Simulated,Experimental))+geom_point(aes(colour = Complex))
ir_calibration_data = read_csv("C:/Users/TT-PC/Google Drive/ral_tddft/IR/sim_ir_comparison.csv")
ir_calibration_data %>% ggplot(aes(Simulated,Experimental))+geom_point(aes(colour = Complex))
ir_calibration_data %>% ggplot(aes(Simulated,Experimental))+geom_point(aes(colour = Complex))+theme_pubr(legend = "left")
ir_calibration_data %>% ggplot(aes(Simulated,Experimental))+geom_point(aes(colour = Complex))
ir_calibration_model = lm(Experimental ~ Simulated, data = ir_calibration_data)
tidy(ir_calibration_model)
ir_calibration_data %>% select(Experimental) %>% add_predictions(ir_calibration_model)
ir_calibration_model
ir_calibration_data %>% select(Simulated) %>% add_predictions(ir_calibration_model)
ir_calibration_data %>% add_predictions(ir_calibration_model)
ir_calibration_data %>% add_predictions(ir_calibration_model) %>% pivot_longer(!Simulated)
ir_calibration_data %>% add_predictions(ir_calibration_model) %>% pivot_longer(!Complex)
ir_calibration_data %>% add_predictions(ir_calibration_model) %>% pivot_longer(!c(Complex, Simulated))
ir_calibration_data %>% add_predictions(ir_calibration_model) %>% pivot_longer(!c(Complex, Simulated)) %>% ggplot(aes(Simulated, value))+geom_point(aes(colour = name))
ir_calibration_data %>% add_predictions(ir_calibration_model) %>% pivot_longer(!c(Complex)) %>% ggplot(aes(Simulated, value))+geom_point(aes(colour = name))
ir_calibration_data %>% add_predictions(ir_calibration_model) %>% pivot_longer(!c(Complex)) %>% ggplot(aes(value))+geom_col(aes(colour = name), position = "dodge")
ir_calibration_data %>% add_predictions(ir_calibration_model) %>% pivot_longer(!c(Complex)) %>% ggplot(aes(x = Complex,y=value))+geom_col(aes(colour = name), position = "dodge")
summary(ir_calibration_model)
ir_calibration_data %>% ggplot(aes(Simulated, Experimental))+geom_smooth(method = "lm")
ir_calibration_data %>% ggplot(aes(Simulated, Experimental))+geom_smooth(method = "lm", alpha = 0)
ir_calibration_data %>% ggplot(aes(Simulated, Experimental))+geom_smooth(method = "lm", alpha = 0, colour = "black")
ir_calibration_data %>% ggplot(aes(Simulated, Experimental))+geom_smooth(method = "lm", alpha = 0, colour = "grey")
ir_calibration_data %>% ggplot(aes(Simulated, Experimental))+geom_point()+geom_smooth(method = "lm", alpha = 0, colour = "grey")
install.packages("nls2")
library(nls2)
ir_calibration_model_nonlinear = nls2(Experimental ~ a*Simulated^2+b*Simulated+c, data = ir_calibration_data)
fo <- y ~ Const + B * (x ^ A)
ir_calibration_data
ir_calibration_model_nonlinear = nls(Experimental ~ a*Simulated^2 + b*Simulated + c, data = ir_calibration_data)
ir_calibration_model_nonlinear = nls(Experimental ~ b*Simulated, data = ir_calibration_data)
ir_calibration_model_nonlinear
ir_calibration_model_nonlinear = nls(Experimental ~ b*Simulated+c, data = ir_calibration_data)
c
d
ir_calibration_model_nonlinear = nls(Experimental ~ b*Simulated+d, data = ir_calibration_data)
ir_calibration_model_nonlinear
ir_calibration_model_nonlinear = nls(Experimental ~ a*Simulated^2+b*Simulated+d, data = ir_calibration_data)
summary(ir_calibration_model_nonlinear)
ir_calibration_model_nonlinear = nls2(Experimental ~ a*Simulated^2+b*Simulated+d, data = ir_calibration_data)
summary(ir_calibration_model_nonlinear)
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear)
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental))+geom_line(aes(Simulated, pred), colour = "red")
ir_calibration_model_nonlinear = nls2(Experimental ~ x*Simulated^3+a*Simulated^2+b*Simulated+d, data = ir_calibration_data)
ir_calibration_model_nonlinear = nls2(Experimental ~ y*Simulated^3+a*Simulated^2+b*Simulated+d, data = ir_calibration_data)
ir_calibration_model_nonlinear = nls2(Experimental ~ y*Simulated^3+a*Simulated^2+b*Simulated+d, data = ir_calibration_data, algorithm = "brute-force")
summary(ir_calibration_model_nonlinear)
y
ir_calibration_model_nonlinear = nls2(Experimental ~ y*Simulated^3+a*Simulated^2+b*Simulated+d, data = ir_calibration_data, algorithm = "brute-force")
ir_calibration_model_nonlinear = nls2(Experimental ~ y*Simulated^3+a*Simulated^2+b*Simulated+d, data = ir_calibration_data,
algorithm = "random-search")
ir_calibration_model_nonlinear = nls2(Experimental ~ y*Simulated^3+a*Simulated^2+b*Simulated+d, data = ir_calibration_data)
ir_calibration_model_nonlinear = nls2(Experimental ~ a*Simulated^2+b*Simulated+d, data = ir_calibration_data)
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental))+geom_line(aes(Simulated, pred), colour = "red")
ir_calibration_model
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental))
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental))+geom_abline(intercept = 0, slope = 1)
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental))+geom_abline(intercept = 0, slope = 0)
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental))+geom_abline(intercept = 0, slope =1.1)
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental))+geom_abline(intercept = 0, slope =1)
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental))+geom_abline(intercept = 0, slope =1)+coord_cartesian(xlim = c(1800, 2100), ylim = c(1800,2100))
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental), size = 1.1)+geom_abline(intercept = 0, slope =1)+coord_cartesian(xlim = c(1800, 2100), ylim = c(1800,2100))
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental), size = 1.5)+geom_abline(intercept = 0, slope =1)+coord_cartesian(xlim = c(1800, 2100), ylim = c(1800,2100))
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental), size = 3)+geom_abline(intercept = 0, slope =1)+coord_cartesian(xlim = c(1800, 2100), ylim = c(1800,2100))
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental), size = 2)+geom_abline(intercept = 0, slope =1)+coord_cartesian(xlim = c(1800, 2100), ylim = c(1800,2100))
momo_pypy_simpath = "C:/Users/TT-PC/Google Drive/ral_tddft/IR/mo_pypy_vib_bp86svp"
read_vibspec(momo_dpa_simpath)
read_vibspec(momo_pypy_simpath) %>%
read_vibspec(momo_pypy_simpath)
read_vibspec(momo_pypy_simpath)
read_vibspec(momo_pypy_simpath) %>% generate_sim_ir(wavenumbers = seq(400,3000))
read_vibspec(momo_pypy_simpath) %>% generate_sim_ir(Wavenumbers = seq(400,3000)) %>% select(Wavenumber,additive) %>% ggplot(aes(Wavenumber, additive))+geom_line()+scale_x_reverse()
read_vibspec(momo_dpa_simpath) %>% generate_sim_ir(Wavenumbers = seq(400,3000)) %>% select(Wavenumber,additive) %>% ggplot(aes(Wavenumber, additive))+geom_line()+scale_x_reverse()
read_vibspec(momo_dpa_simpath) %>% generate_sim_ir(Wavenumbers = seq(400,3000)) %>% pivot_longer(!Wavenumber) %>% ggplot(aes(Wavenumber, value))+geom_line(aes(colour = name))+scale_x_reverse()
read_vibspec(momo_dpa_simpath) %>% generate_sim_ir(Wavenumbers = seq(400,3000)) %>% pivot_longer(!Wavenumber) %>% ggplot(aes(Wavenumber, value))+geom_line(aes(colour = name))+scale_x_reverse()+theme(legend.position = "none")
dpa_allir = read_vibspec(momo_dpa_simpath) %>% generate_sim_ir(Wavenumbers = seq(400,3000)) %>% pivot_longer(!Wavenumber) %>% ggplot(aes(Wavenumber, value))+geom_line(aes(colour = name))+scale_x_reverse()+theme(legend.position = "none")
ggplotly(dpa_allir)
pypy_allir = read_vibspec(momo_pypy_simpath) %>% generate_sim_ir(Wavenumbers = seq(400,3000)) %>% pivot_longer(!Wavenumber) %>% ggplot(aes(Wavenumber, value))+geom_line(aes(colour = name))+scale_x_reverse()+theme(legend.position = "none")
pypy_allir %>% ggplotly()
ir_calibration_data %>% add_predictions(ir_calibration_model_nonlinear) %>% ggplot()+geom_point(aes(Simulated, Experimental))+geom_abline(intercept = 0, slope =1)
ir_calibration_data %>% ggplot()+geom_point(aes(Simulated, Experimental), size = 1.5)+geom_abline(intercept = 0, slope =1, colour = "grey")
ir_calibration_data %>% ggplot()+geom_point(aes(Simulated, Experimental), size = 1.5)+geom_abline(intercept = 0, slope =1, colour = "black")
simex_comparison_plot = ir_calibration_data %>%
ggplot()+geom_point(aes(Simulated, Experimental), size = 1.5)+
geom_abline(intercept = 0, slope =1, colour = "black")+
coord_cartesian(xlim=c(1850,2250), ylim = c(1850,2250))+
labs(x = paste0("Simulated",  "\U207b", "\U00B9"), y =  paste0("Experimental",  "\U207b", "\U00B9"))
simex_comparison_plot
simex_comparison_plot = ir_calibration_data %>%
ggplot()+geom_point(aes(Simulated, Experimental), size = 1.5)+
geom_abline(intercept = 0, slope =1, colour = "black")+
coord_cartesian(xlim=c(1850,2250), ylim = c(1850,2200))+
labs(x = paste0("Simulated",  "\U207b", "\U00B9"), y =  paste0("Experimental",  "\U207b", "\U00B9"))
simex_comparison_plot+geom_smooth(method = lm, se =F)
simex_comparison_plot_2 = ir_calibration_data %>%
ggplot()+geom_point(aes(Simulated, Experimental), size = 1.5)+
geom_abline(intercept = 0, slope =1, colour = "black")+
coord_cartesian(xlim=c(1850,2250), ylim = c(1850,2250))+
labs(x = paste0("Simulated",  "\U207b", "\U00B9"), y =  paste0("Experimental",  "\U207b", "\U00B9"))+geom_smooth(method = "lm", se = F)
simex_comparison_plot_2
simex_comparison_plot_2 = ir_calibration_data %>%
ggplot(aes(Simulated, Experimental))+geom_point(aes(Simulated, Experimental), size = 1.5)+
geom_abline(intercept = 0, slope =1, colour = "black")+
coord_cartesian(xlim=c(1850,2250), ylim = c(1850,2250))+
labs(x = paste0("Simulated",  "\U207b", "\U00B9"), y =  paste0("Experimental",  "\U207b", "\U00B9"))+geom_smooth(method = "lm", se = F)
simex_comparison_plot_2
simex_comparison_plot = ir_calibration_data %>%
ggplot(aes(Simulated, Experimental))+geom_point(size = 1.5)+
geom_abline(intercept = 0, slope =1, colour = "black")+
coord_cartesian(xlim=c(1850,2250), ylim = c(1850,2200))+
labs(x = paste0("Simulated / cm",  "\U207b", "\U00B9"), y =  paste0("Experimental / cm",  "\U207b", "\U00B9"))
simex_comparison_plot
simex_comparison_plot+geom_smooth(colour = "grey", se = F, method = "lm")
simex_comparison_plot = ir_calibration_data %>%
ggplot(aes(Simulated, Experimental))+geom_point(size = 1.5)+
#geom_abline(intercept = 0, slope =1, colour = "black")+
coord_cartesian(xlim=c(1850,2250), ylim = c(1850,2200))+
labs(x = paste0("Simulated / cm",  "\U207b", "\U00B9"), y =  paste0("Experimental / cm",  "\U207b", "\U00B9"))
simex_comparison_plot = ir_calibration_data %>%
ggplot(aes(Simulated, Experimental))+geom_point(size = 1.5)+
#geom_abline(intercept = 0, slope =1, colour = "black")+
coord_cartesian(xlim=c(1850,2150), ylim = c(1850,2150))+
labs(x = paste0("Simulated / cm",  "\U207b", "\U00B9"), y =  paste0("Experimental / cm",  "\U207b", "\U00B9"))
simex_comparison_plot
simex_comparison_plot+geom_abline(intercept = 0, slope = 1)
simex_comparison_plot+geom_smooth(method = "lm", colour = "darkred", alpha = 0.75, se = F)
simex_comparison_plot+geom_line(stat = "smooth")
simex_comparison_plot+geom_line(stat = "smooth", method = lm, linetype = 3, colour = "darkred")
ir_calibration_model %>% tidy()
summary(ir_calibration_model)
ir_calibration_data_fitted = ir_calibration_data %>% add_predictions(ir_calibration_model)
simex_comparison_plot+geom_abline(intercept = 0, slope = 1)+geom_point(data = ir_calibration_data_fitted, aes(Simulated, pred))
simex_comparison_plot+geom_abline(intercept = 0, slope = 1)+geom_point(data = ir_calibration_data_fitted, aes(Simulated, pred), colour = "red")
ir_calibration_data_fitted
ir_calibration_frame
ir_calibration_model
simex_comparison_plot+geom_abline(intercept = 0, slope = 1)+geom_point(data = ir_calibration_data_fitted, aes(Simulated, pred), colour = "red") -> p3
ggplotly(p3)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% ggplot(aes(Experimental, scaled))+geom_point()
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% ggplot(aes(Experimental, scaled))+geom_point()+geom_abline(intercept = 0, slope = 1)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% pivot_longer(!C(Complex, Simulated)) %>% ggplot(aes(Simulated, name))+geom_point(aes(colour = value))+geom_abline(intercept =0, slope = 1)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% pivot_longer(!c(Complex, Simulated)) %>% ggplot(aes(Simulated, name))+geom_point(aes(colour = value))+geom_abline(intercept =0, slope = 1)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% pivot_longer(!c(Complex, Simulated)) %>% ggplot(aes(Simulated, value))+geom_point(aes(colour = names))+geom_abline(intercept =0, slope = 1)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% pivot_longer(!C(Complex, Simulated)) %>% ggplot(aes(Simulated, value))+geom_point(aes(colour = name))+geom_abline(intercept =0, slope = 1)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% pivot_longer(!c(Complex, Simulated)) %>% ggplot(aes(Simulated, value))+geom_point(aes(colour = name))+geom_abline(intercept =0, slope = 1)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% ggplot(aes(Experimental, scaled))+geom_point()
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% pivot_longer(!C(Complex, Experimental)) %>% ggplot(aes(Simulated, value))+geom_point(aes(colour = name))+geom_abline(intercept =0, slope = 1)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% pivot_longer(!c(Complex, Experimental)) %>% ggplot(aes(Simulated, value))+geom_point(aes(colour = name))+geom_abline(intercept =0, slope = 1)
ir_calibration_data_fitted %>% mutate(scaled = (1.21*Simulated)-452.7) %>% pivot_longer(!c(Complex, Experimental)) %>% ggplot(aes(Experimental, value))+geom_point(aes(colour = name))+geom_abline(intercept =0, slope = 1)
ir_calibration_data_fitted %>% ggplot(aes(pred, Experimental))+geom_point()
ir_calibration_data_fitted %>% ggplot(aes(pred, Experimental))+geom_point()+geom_abline(intercept = 0, slope = 1)
ir_calibration_data_fitted = ir_calibration_data %>% add_predictions(ir_calibration_model) %>%
rename("Fitted" = pred)
ir_calibration_data_fitted %>% ggplot(aes(Fitted, Experimental))+
coord_cartesian(xlim=c(1850,2150), ylim = c(1850,2150))+
labs(x = paste0("Fitted / cm",  "\U207b", "\U00B9"), y =  paste0("Experimental / cm",  "\U207b", "\U00B9"))
ir_calibration_data_fitted %>% ggplot(aes(Fitted, Experimental))+
geom_point(size = 1.5)+
geom_abline(intercept = 0, slope = 1)
View(jmlral936)
coord_cartesian(xlim=c(1800,2150), ylim = c(1800,2150))+
labs(x = paste0("Fitted / cm",  "\U207b", "\U00B9"), y =  paste0("Experimental / cm",  "\U207b", "\U00B9"))
ir_calibration_data_fitted %>% ggplot(aes(Fitted, Experimental))+
geom_point(size = 1.5)+
geom_abline(intercept = 0, slope = 1)+
coord_cartesian(xlim=c(1800,2150), ylim = c(1800,2150))+
labs(x = paste0("Fitted / cm",  "\U207b", "\U00B9"), y =  paste0("Experimental / cm",  "\U207b", "\U00B9"))
simulate_vibspec = function(simpath){
ir_band_info = generate_sim_ir(read_vibspec(simpath), Wavenumbers = seq(400,3000))
ir_band_info %>% select(Wavenumber, "Absorbance" = additive) %>% ggplot(aes(Wavenumber, Absorbance))+
geom_line(linetype = 2)
}
simulate_vibspec("C:/Users/TT-PC/Google Drive/ral_tddft/IR/pp2_iso4_vibspec.txt")
produce_comparison_plot = function(simpath, expath, scale_factor = F){
if(grepl(".dpt", expath)){
exp =read_delim(expath, delim = "\t", col_names = c("Wavenumber", "Absorbance"))
}else if(grepl(".csv", expath)){
exp = read_csv(expath, col_names = c("Wavenumber", "Absorbance"))
}else{
print("Unsupported filetype, please check - Exiting")
return(0)
}
sim_parsed = read_vibspec(simpath)
if(scale_factor != F){
sim_parsed = sim_parsed %>% rename("Simulated" = Wavenumber) %>% add_predictions(scale_factor) %>%
rename("Wavenumber" = pred)
}
sim = generate_sim_ir(sim_parsed,
scale = 1,
peakwidth = 5,
Wavenumbers = exp$Wavenumber)
ir_frame_full = sim %>% select(Wavenumber, additive) %>% full_join(exp)
comparison_plot = ir_frame_full %>% filter(between(Wavenumber, 1650, 2150)) %>% rename("Simulated"="additive", "Experimental"= "Absorbance") %>%
mutate(across(!Wavenumber, rescale_norm)) %>%
pivot_longer(!Wavenumber, names_to = "Spectrum", values_to = "Absorbance") %>%
ggplot(aes(Wavenumber,Absorbance))+geom_line(aes(colour=Spectrum, linetype =Spectrum), linewidth =0.8)+theme_pubr(base_family = "Arial", base_size = 16)+scale_x_reverse()+
scale_color_brewer(palette= "Set1")+theme(legend.title = element_blank())+
labs(
x = paste0("Wavenumber / cm", "\U207b", "\U00B9"),
y = "Normalised Intensity")
return(comparison_plot)
}
produce_comparison_plot(coco_pypy_simpath, coco_pypy_expath)
produce_comparison_plot(coco_pypy_simpath, coco_pypy_expath, scale = ir_calibration_model)
produce_comparison_plot = function(simpath, expath, scale_factor = F){
if(grepl(".dpt", expath)){
exp =read_delim(expath, delim = "\t", col_names = c("Wavenumber", "Absorbance"))
}else if(grepl(".csv", expath)){
exp = read_csv(expath, col_names = c("Wavenumber", "Absorbance"))
}else{
print("Unsupported filetype, please check - Exiting")
return(0)
}
sim_parsed = read_vibspec(simpath)
if(scale_factor != F){
sim_parsed = sim_parsed %>% rename("Simulated" = Wavenumber) %>% add_predictions(scale_factor) %>%
rename("Wavenumber" = pred)
return(sim_parsed)
}
sim = generate_sim_ir(sim_parsed,
scale = 1,
peakwidth = 5,
Wavenumbers = exp$Wavenumber)
ir_frame_full = sim %>% select(Wavenumber, additive) %>% full_join(exp)
comparison_plot = ir_frame_full %>% filter(between(Wavenumber, 1650, 2150)) %>% rename("Simulated"="additive", "Experimental"= "Absorbance") %>%
mutate(across(!Wavenumber, rescale_norm)) %>%
pivot_longer(!Wavenumber, names_to = "Spectrum", values_to = "Absorbance") %>%
ggplot(aes(Wavenumber,Absorbance))+geom_line(aes(colour=Spectrum, linetype =Spectrum), linewidth =0.8)+theme_pubr(base_family = "Arial", base_size = 16)+scale_x_reverse()+
scale_color_brewer(palette= "Set1")+theme(legend.title = element_blank())+
labs(
x = paste0("Wavenumber / cm", "\U207b", "\U00B9"),
y = "Normalised Intensity")
return(comparison_plot)
}
produce_comparison_plot(coco_pypy_simpath, coco_pypy_expath, scale = ir_calibration_model)
read_vibspec(coco_pypy_simpath)
read_vibspec(coco_pypy_simpath) %>% rename("Simulated = Wavenumber")
read_vibspec(coco_pypy_simpath) %>% rename("Simulated" = Wavenumber)
read_vibspec(coco_pypy_simpath) %>% rename("Simulated" = Wavenumber) %>% add_predictions(ir_calibration_model)
read_vibspec(coco_pypy_simpath) %>% rename("Simulated" = Wavenumber) %>% add_predictions(ir_calibration_model) %>% rename("Wavenumber"=pred)
read_vibspec(coco_pypy_simpath) %>% rename("Simulated" = Wavenumber) %>% add_predictions(ir_calibration_model) %>% rename("Wavenumber"=pred) -> yt
generate_sim_ir(yt, Wavenumbers = seq(1800,2200))
generate_sim_ir(yt, Wavenumbers = seq(1800,2200)) %>% ggplot(aes(Wavenumber, additive))+geom_line()
typeof(F)
produce_comparison_plot = function(simpath, expath, scale_factor = F){
if(grepl(".dpt", expath)){
exp =read_delim(expath, delim = "\t", col_names = c("Wavenumber", "Absorbance"))
}else if(grepl(".csv", expath)){
exp = read_csv(expath, col_names = c("Wavenumber", "Absorbance"))
}else{
print("Unsupported filetype, please check - Exiting")
return(0)
}
sim_parsed = read_vibspec(simpath)
if(typeof(scale_factor)!="logical"){
sim_parsed = sim_parsed %>% rename("Simulated" = Wavenumber) %>% add_predictions(scale_factor) %>%
rename("Wavenumber" = pred)
return(sim_parsed)
}
sim = generate_sim_ir(sim_parsed,
scale = 1,
peakwidth = 5,
Wavenumbers = exp$Wavenumber)
ir_frame_full = sim %>% select(Wavenumber, additive) %>% full_join(exp)
comparison_plot = ir_frame_full %>% filter(between(Wavenumber, 1650, 2150)) %>% rename("Simulated"="additive", "Experimental"= "Absorbance") %>%
mutate(across(!Wavenumber, rescale_norm)) %>%
pivot_longer(!Wavenumber, names_to = "Spectrum", values_to = "Absorbance") %>%
ggplot(aes(Wavenumber,Absorbance))+geom_line(aes(colour=Spectrum, linetype =Spectrum), linewidth =0.8)+theme_pubr(base_family = "Arial", base_size = 16)+scale_x_reverse()+
scale_color_brewer(palette= "Set1")+theme(legend.title = element_blank())+
labs(
x = paste0("Wavenumber / cm", "\U207b", "\U00B9"),
y = "Normalised Intensity")
return(comparison_plot)
}
produce_comparison_plot(coco_pypy_simpath, coco_pypy_expath, scale = ir_calibration_model)
produce_comparison_plot(coco_pypy_simpath, coco_pypy_expath, scale = ir_calibration_model)
produce_comparison_plot = function(simpath, expath, scale_factor = F){
if(grepl(".dpt", expath)){
exp =read_delim(expath, delim = "\t", col_names = c("Wavenumber", "Absorbance"))
}else if(grepl(".csv", expath)){
exp = read_csv(expath, col_names = c("Wavenumber", "Absorbance"))
}else{
print("Unsupported filetype, please check - Exiting")
return(0)
}
sim_parsed = read_vibspec(simpath)
if(typeof(scale_factor)!="logical"){
sim_parsed = sim_parsed %>% rename("Simulated" = Wavenumber) %>% add_predictions(scale_factor) %>%
rename("Wavenumber" = pred)
}
sim = generate_sim_ir(sim_parsed,
scale = 1,
peakwidth = 5,
Wavenumbers = exp$Wavenumber)
ir_frame_full = sim %>% select(Wavenumber, additive) %>% full_join(exp)
comparison_plot = ir_frame_full %>% filter(between(Wavenumber, 1650, 2150)) %>% rename("Simulated"="additive", "Experimental"= "Absorbance") %>%
mutate(across(!Wavenumber, rescale_norm)) %>%
pivot_longer(!Wavenumber, names_to = "Spectrum", values_to = "Absorbance") %>%
ggplot(aes(Wavenumber,Absorbance))+geom_line(aes(colour=Spectrum, linetype =Spectrum), linewidth =0.8)+theme_pubr(base_family = "Arial", base_size = 16)+scale_x_reverse()+
scale_color_brewer(palette= "Set1")+theme(legend.title = element_blank())+
labs(
x = paste0("Wavenumber / cm", "\U207b", "\U00B9"),
y = "Normalised Intensity")
return(comparison_plot)
}
produce_comparison_plot(coco_pypy_simpath, coco_pypy_expath, scale = ir_calibration_model)
produce_comparison_plot(mo_dpa_simpath, mo_dpa_expath, scale = ir_calibration_model)
momo_dpa_expath = "C:/Users/TT-PC/Google Drive/ral_tddft/IR/TT65f1.csv"
momo_dpa_simpath = "C:/Users/TT-PC/Google Drive/ral_tddft/IR/mo_dpa_vib_bp86svp"
produce_comparison_plot(mo_dpa_simpath, mo_dpa_expath, scale = ir_calibration_model)
produce_comparison_plot(mo_dpa_simpath, mo_dpa_expath, scale_facator = ir_calibration_model)
produce_comparison_plot = function(simpath, expath, scale_factor = F){
if(grepl(".dpt", expath)){
exp =read_delim(expath, delim = "\t", col_names = c("Wavenumber", "Absorbance"))
}else if(grepl(".csv", expath)){
exp = read_csv(expath, col_names = c("Wavenumber", "Absorbance"))
}else{
print("Unsupported filetype, please check - Exiting")
return(0)
}
sim_parsed = read_vibspec(simpath)
if(typeof(scale_factor)!="logical"){
sim_parsed = sim_parsed %>% rename("Simulated" = Wavenumber) %>% add_predictions(scale_factor) %>%
rename("Wavenumber" = pred)
}
sim = generate_sim_ir(sim_parsed,
scale = 1,
peakwidth = 5,
Wavenumbers = exp$Wavenumber)
ir_frame_full = sim %>% select(Wavenumber, additive) %>% full_join(exp)
comparison_plot = ir_frame_full %>% filter(between(Wavenumber, 1650, 2150)) %>% rename("Simulated"="additive", "Experimental"= "Absorbance") %>%
mutate(across(!Wavenumber, rescale_norm)) %>%
pivot_longer(!Wavenumber, names_to = "Spectrum", values_to = "Absorbance") %>%
ggplot(aes(Wavenumber,Absorbance))+geom_line(aes(colour=Spectrum, linetype =Spectrum), linewidth =0.8)+theme_pubr(base_family = "Arial", base_size = 16)+scale_x_reverse()+
scale_color_brewer(palette= "Set1")+theme(legend.title = element_blank())+
labs(
x = paste0("Wavenumber / cm", "\U207b", "\U00B9"),
y = "Normalised Intensity")
return(comparison_plot)
}
produce_comparison_plot(mo_dpa_simpath, mo_dpa_expath, scale_facator = ir_calibration_model)
produce_comparison_plot(coco_pypy_simpath, coco_pypy_expath, scale = ir_calibration_model)
produce_comparison_plot(coco_pypy_simpath, coco_pypy_expath, scale_factor =  ir_calibration_model)
produce_comparison_plot(momo_dpa_simpath, momo_dpa_expath, scale_factor =  ir_calibration_model)
produce_comparison_plot(momo_pypy_simpath, momo_pypy_expath, scale_factor =  ir_calibration_model)
simulate_vibspec = function(simpath, scale_factor = F, xmin =400, xmax = 3000){
ir_band_info = generate_sim_ir(read_vibspec(simpath), Wavenumbers = seq(400,3000), scale_factor = scale_factor) %>%
filter(between(Wavenumber, xmin, xmax)) %>% mutate(additive = rescale_norm(additive))
ir_band_info %>% select(Wavenumber, "Absorbance" = additive) %>% ggplot(aes(Wavenumber, Absorbance))+
geom_line(linetype = 2)
}
simulate_vibspec("C:/Users/TT-PC/Google Drive/ral_tddft/IR/pp2_iso4_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1850, xmax = 2050)
simulate_vibspec = function(simpath, scale_factor = F, xmin =400, xmax = 3000){
sim_parsed = read_vibspec(simpath)
if(typeof(scale_factor)!="logical"){
sim_parsed = sim_parsed %>% rename("Simulated" = Wavenumber) %>% add_predictions(scale_factor) %>%
rename("Wavenumber" = pred)
}
ir_band_info = generate_sim_ir(sim_parsed, Wavenumbers = seq(400,3000)) %>%
filter(between(Wavenumber, xmin, xmax)) %>% mutate(additive = rescale_norm(additive))
ir_band_info %>% select(Wavenumber, "Absorbance" = additive) %>% ggplot(aes(Wavenumber, Absorbance))+
geom_line(linetype = 2)
}
simulate_vibspec("C:/Users/TT-PC/Google Drive/ral_tddft/IR/pp2_iso4_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1850, xmax = 2050)
simulate_vibspec("C:/Users/TT-PC/Google Drive/ral_tddft/IR/pp2_iso4_vibspec.txt", scale_factor = F, xmin = 1850, xmax = 2050)
simulate_vibspec("C:/Users/TT-PC/Google Drive/ral_tddft/IR/pp2_iso4_vibspec.txt", scale_factor = F, xmin = 1800, xmax = 2050)
simulate_vibspec("C:/Users/TT-PC/Google Drive/ral_tddft/IR/pp2_iso4_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1800, xmax = 2050)
simulate_vibspec("C:/Users/TT-PC/Google Drive/ral_tddft/IR/pp1_iso4_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1800, xmax = 2050)
simulate_vibspec("C:/Users/TT-PC/Google Drive/ral_tddft/IR/pp3_iso4_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1800, xmax = 2050)
simulate_vibspec("C:/Users/TT-PC/Google Drive/ral_tddft/IR/pp4_iso4_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1800, xmax = 2050)
source("C:/Users/TT-PC/Google Drive/multi_surface_plotter.R")
library(tidyverse)
library(ggpubr)
library(broom)
library(modelr)
library(cowplo)
library(cowplot)
pypy_uf_energies = import_surface("C:/users/tt-pc/google drive/ral_tddft/mopypy_ultrafast_energies.csv")
pypy_uf_energies = import_surface("C:/users/tt-pc/google drive/ral_tddft/mo_pypy_ultrafast_energies.csv")
plot_multisurface(pypy_uf_energies, linelabels = T, lineenergies = T, monochrome = T)
getwd()
setwd("C:/users/tt-pc/google drive/ral_tddft/ir")
mopypy_pp4_1_spec_mco5 = simulate_vibspec("mopypy_pp4_iso1_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1862, xmax = 2056)+labs(subtitle = "PP4 iso1")
mopypy_pp4_1_spec_mco5 = simulate_vibspec("mopypy_pp4_iso2_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1862, xmax = 2056)+labs(subtitle = "PP4 iso2")
mopypy_pp4_1_spec_mco5 = simulate_vibspec("mopypy_pp4_iso3_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1862, xmax = 2056)+labs(subtitle = "PP4 iso3")
mopypy_pp4_1_spec_mco5 = simulate_vibspec("mopypy_pp4_iso4_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1862, xmax = 2056)+labs(subtitle = "PP4 iso4")
mopypy_pp4_1_spec_mco5 = simulate_vibspec("mopypy_pp4_iso1_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1862, xmax = 2056)+labs(subtitle = "PP4 iso1")
mopypy_pp4_2_spec_mco5 = simulate_vibspec("mopypy_pp4_iso2_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1862, xmax = 2056)+labs(subtitle = "PP4 iso2")
mopypy_pp4_3_spec_mco5 = simulate_vibspec("mopypy_pp4_iso3_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1862, xmax = 2056)+labs(subtitle = "PP4 iso3")
mopypy_pp4_4_spec_mco5 = simulate_vibspec("mopypy_pp4_iso4_vibspec.txt", scale_factor = ir_calibration_model, xmin = 1862, xmax = 2056)+labs(subtitle = "PP4 iso4")
plot_grid(mopypy_pp4_1_spec_mco5,mopypy)
plot_grid(mopypy_pp4_1_spec_mco5,mopypy_pp4_2_spec_mco5, mopypy_pp4_3_spec_mco5, mopypy_pp4_4_spec_mco5)
theme_set(theme_pubr(base_size = 16))
plot_grid(mopypy_pp4_1_spec_mco5,mopypy_pp4_2_spec_mco5, mopypy_pp4_3_spec_mco5, mopypy_pp4_4_spec_mco5, ncol = 1)
jmlral1248_proc = read_csv("E:/Ral_data/jmlral1248_baselined/jmlral1248_baselined_processed.sv")
jmlral1248_proc = read_csv("E:/Ral_data/jmlral1248_baselined/jmlral1248_baselined_processed.csv")
mopypy_ncmecoord = plot_spectrum_at_time(jmlral1248_proc, 10)
plot_grid(mopypy_pp4_1_spec_mco5,mopypy_pp4_2_spec_mco5, mopypy_pp4_3_spec_mco5, mopypy_pp4_4_spec_mco5, mopypy_ncmecoord, ncol = 1)
plot_spectrum_at_time(jmlral1248_proc , 1)
plot_spectrum_at_time(jmlral1248_proc , 5)
plot_spectrum_at_time(jmlral1248_proc , 0.5)
setwd("C:/Users/tt730/google drive/experimental/thesis si/thesis_modelling")
source("C:/Users/tt730/Google Drive/Experimental/Thesis SI/Thesis_modelling/final_modelling.R", echo=TRUE)
source("C:/Users/tt730/Google Drive/Experimental/Thesis SI/Thesis_modelling/final_modelling.R", echo=TRUE)
summary(thesis_model_trunc)
source("C:/Users/tt730/Google Drive/Experimental/Thesis SI/Thesis_modelling/final_modelling.R", echo=TRUE)
