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# to get the original version of the eCO2 file | |
download.file('https://github.com/cran/SoilR/raw/1.1-23/data/eCO2.rda', | |
'eCO2.rda') | |
load('eCO2.rda') | |
library(SoilR) | |
library(rstan) | |
totalC_t0 <- 7.7; # not included in data, so hard code here | |
t0 <- 0; | |
N_t <- 25; # calculated by inspection |
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library(tidyverse) | |
# x <- read_csv('C:/Users/David/Downloads/Untitled spreadsheet - Sheet1.csv') | |
# | |
# colnames(x) | |
# dim(x) | |
# | |
# xx <- x %>% | |
# rename(organ = `Plant tissue`, | |
# soil = `Soil \n Concentration`, | |
# bcf = BCF) %>% |
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select extract(year from created_at) as year, container_type, count(*) as n from dbfiles where id between 1000000000 and 2000000000 group by container_type, year order by container_type, year; | |
select created_user_id, container_type, count(*) as n from dbfiles where id between 1000000000 and 2000000000 group by container_type, created_user_id order by n desc; | |
select * from dbfiles where created_user_id is not null; | |
select extract(year from created_at) as year, created_user_id, container_type, count(*) as n from dbfiles where id between 9000000000 and 10000000000 group by container_type, created_user_id, year order by year, container_type, created_user_id; | |
select null as year, created_user_id, container_type, count(*) as n from dbfiles where id between 9000000000 and 10000000000 group by container_type, created_user_id order by container_type, created_user_id; |
We can't make this file beautiful and searchable because it's too large.
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"site_id","sitename","lon","lat","format_name","file_path","filename","start_date","end_date","duration","created_at" | |
"1000026939","Moody AFB","-83.1753","30.984401","ed.met_driver_header files format","/projectnb/dietzelab/mccabete/test_pecan/cogongrass_projections/GFDL/moody_AFB/only_co2/ED_driver/Ed_driver/","Cmip5_RCP8.5_Present_day_temp","1/1/2006 06:00:00","1/1/2100 05:59:00","93 years 11 mons 30 days 23:59:00","6/4/2022 18:19:38.277567" | |
"1000026939","Moody AFB","-83.1753","30.984401","ed.met_driver_header files format","/projectnb/dietzelab/mccabete/test_pecan/cogongrass_projections/GFDL/moody_AFB/with_co2/ED_driver/","CMIP5_RCP8.5_with_Co2","1/1/2006 06:00:00","13/12/2100 05:59:00","94 years 11 mons 11 days 23:59:00","6/4/2022 18:17:08.145333" | |
"1000026939","Moody AFB","-83.1753","30.984401","ed.met_driver_header files format","/projectnb/dietzelab/mccabete/test_pecan/cogongrass_projections/GFDL/moody_AFB/resampled_present_day/ED_driver","Cmip5_Present_day","1/1/2006 06:00:00","1/1/2100 05:59:00","93 y |
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library(traits) | |
library(dplyr) | |
library(ggplot2) | |
options(betydb_url = 'https://www.betydb.org/', | |
betydb_api_version = 'v1' | |
) | |
yields <- betydb_search(result_type = 'yields', genus = 'Panicum', limit = 'none') | |
treatments <- betydb_query(table = 'treatments', limit = 'none') %>% | |
dplyr::mutate(treatment_id = id) %>% |
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select | |
site_id, | |
sites.sitename, | |
st_X(sites.geometry) as lon, | |
st_y(sites.geometry) as lat, | |
formats.name as format_name, | |
dbfiles.file_path as file_path, | |
inputs.name as filename, | |
inputs.start_date, | |
inputs.end_date, |
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library(PEcAn.DB) | |
# given PFT name but no model name??? | |
# | |
# if select count(*) from pfts where name= <PFT name> > 1 then | |
# select modeltype_id from pfts where name = <PFT name> | |
# if > 1 modeltype_id | |
# select name from modeltypes where id = modeltype_id | |
# return these model names as messsage asking which one user wants |
We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 38 columns, instead of 19. in line 1.
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"ensemble","npp","site","mort2","growth_resp_factor","leaf_turnover_rate","leaf_width","nonlocal_dispersal","fineroot2leaf","root_turnover_rate","seedling_mortality","stomatal_slope","quantum_efficiency","Vcmax","r_fract","cuticular_cond","root_respiration_rate","Vm_low_temp","SLA","mean_annual_temp","mean_diurnal_range","isothermality","temp_seasonality","max_temp_warmest month","min_temp_coldest_month","temp_annual_range","mean_temp_wettest_quarter","mean_temp_driest_quarter","mean_temp_warmest_quarter","mean_temp_coldest_quarter","mean_annual_precip","precip_wettest_month","precip_driest_month","precip_seasonality","precip_wettest_quarter","precip_driest_quarter","precip_warmest_quarter","precip_coldest_quarter" | |
"1",3.28794455528259,"WL",13.4941024361116,0.199588976581073,5.22541143636259,4.54482864260038,0.160406564989664,2.80249135407987,0.158750654546649,0.964989573638797,3.78549864797161,0.0563503122493127,20.7691338359971,0.230258975287167,20938.2246204727,7.90012049814983,10.8950676841584,39.22618746 |
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{ | |
"dimensions": { | |
"wavelength": 939 | |
}, | |
"variables": { | |
"wavelength": { | |
"shape": ["wavelength"], | |
"type": "double", | |
"attributes": { | |
"long_name": "Hyperspectral Wavelength", |
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