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DownloadData.py
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DownloadData.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
import cdms2 as cdms
import MV2 as MV
import cdtime,cdutil,genutil
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import string
import glob
import scipy.stats as stats
# Local solution
# If running remotely, uncomment the following code:
# %%bash
# git clone https://github.com/katemarvel/CMIP5_tools
# import CMIP5_tools as cmip5
import sys,os
#sys.path.append("/Users/kmarvel/Google Drive/python-utils")
sys.path.append("../python-utils")
import CMIP5_tools as cmip5
import DA_tools
import Plotting
from eofs.cdms import Eof
from eofs.multivariate.cdms import MultivariateEof
get_ipython().run_line_magic('matplotlib', 'inline')
import requests
import pandas as pd
### Set classic Netcdf (ver 3)
cdms.setNetcdfShuffleFlag(0)
cdms.setNetcdfDeflateFlag(0)
cdms.setNetcdfDeflateLevelFlag(0)
#external_drive='/Volumes/CMIP6/'
external_drive="/home/kdm2144/"
# In[2]:
NCA4regions={}
#Northwest (NW): (125°W–111°W, 42°N–49°N)
NCA4regions["NW"]=cdutil.region.domain(longitude=(-125,-111),latitude=(42,49))
#Southwest (SW): (124°W–102°W, 31°N–42°N)
NCA4regions["SW"]=cdutil.region.domain(longitude=(-124,-102),latitude=(31,42))
#Upper Great Plains (GPu): (116°W–95°W, 40°N–49°N)
NCA4regions["GPu"]=cdutil.region.domain(longitude=(-116,-95),latitude=(40,49))
#Lower Great Plains (GPl): (107°W–93°W, 26°N–40°N)
NCA4regions["GPl"]=cdutil.region.domain(longitude=(-107,-93),latitude=(26,40))
#Midwest (MW): (97°W–80°W, 36°N–50°N)
NCA4regions["MW"]=cdutil.region.domain(longitude=(-97,-80),latitude=(36,50))
#Northeast (NE): (82°W–67°W, 37°N–48°N)
NCA4regions["NE"]=cdutil.region.domain(longitude=(-82,-67),latitude=(37,48))
#Southeast (SE): (95°W–76°W, 25°N–39°N)
NCA4regions["SE"]=cdutil.region.domain(longitude=(-95,-76),latitude=(25,39))
# In[3]:
def pull_data(curr_mod,curr_var,experiment_id,member_id,overwrite=False):
# Baseline directory
base_dir = 'http://mary.ldeo.columbia.edu:81/CMIP6/.'
# Write directory
base_write_dir= external_drive+'DROUGHT/DOWNLOADED_RAW/'
df_proclist = pd.DataFrame(columns=['model','sim','ensemble','variable'])
ingrid_cmip6 = pd.read_csv("~/mary_cmip6.csv")
write_dir = base_write_dir+curr_var+"/"+curr_mod+"/"
write_stem = curr_var+"."+experiment_id+"."+curr_mod+"."+member_id+".*.nc"
#If the directory doesn't exist already, make it
os.makedirs(os.path.join(base_write_dir, curr_var, curr_mod),exist_ok=True)
if not overwrite:
already_exist=glob.glob(write_dir+write_stem)
if len(already_exist)!=0:
#print("Already done!")
return False
#rips=np.unique(np.array(df1.member_id))
df1 = ingrid_cmip6[(ingrid_cmip6.source_id==curr_mod) & (ingrid_cmip6.variable_id==curr_var) & (ingrid_cmip6.experiment_id == experiment_id)& (ingrid_cmip6.member_id == member_id)]
#Construct openDAP link
nfiles,nidentifiers=df1.shape
times=np.sort(np.array(df1.time_range))
i_ens=np.where(df1.time_range==times[0])[0]
time_range=times[0]
for time_range in times:
# Construct Remote OpenDAP Link
i_ens=np.where(df1.time_range==time_range)[0][0]
nc_link = base_dir+df1.activity_id.iloc[i_ens]+'/.'+df1.institution_id.iloc[i_ens]+'/.'+curr_mod+'/.'+experiment_id+'/.'+df1.member_id.iloc[i_ens]+'/.'+df1.table_id.iloc[i_ens]+ '/.'+curr_var+'/.'+df1.grid_label.iloc[i_ens]+'/.'+df1.version.iloc[i_ens]+'/.'+df1.file_basename.iloc[i_ens]+'/.'+curr_var+'/dods'
request = requests.get(nc_link)
if request.status_code == 200:
#Get the data
f=cdms.open(nc_link)
data=f(curr_var)
tax=data.getTime()
tax.id="time"
latax=data.getLatitude()
lonax=data.getLongitude()
#reshape it to years and months
ntime=data.shape[0]
nyears=int(ntime/12)
rdata=data.reshape((nyears,12)+data.shape[1:])
for i in range(nyears):
yeardata=rdata[i]
#Make the time axis
tax_trunc=cdms.createAxis(tax[12*i:12*(i+1)])
tax_trunc.designateTime()
for key in tax.attributes.keys():
setattr(tax_trunc,key,tax.attributes[key])
yeardata.setAxis(0,tax_trunc)
yeardata.setAxis(1,latax)
yeardata.setAxis(2,lonax)
#get the start year for labeling purposes
year=str(tax_trunc.asComponentTime()[0].year)
writename = curr_var+"."+experiment_id+"."+curr_mod+"."+member_id+"."+year.zfill(4)+".nc"
fw=cdms.open(write_dir+writename,"w")
fw.write(yeardata)
fw.close()
f.close()
return True
# In[4]:
def check_availability(curr_mod,curr_var,experiment_id):
ingrid_cmip6 = pd.read_csv("~/mary_cmip6.csv")
df1 = ingrid_cmip6[(ingrid_cmip6.source_id==curr_mod) & (ingrid_cmip6.variable_id==curr_var) & (ingrid_cmip6.experiment_id == experiment_id)]
return(df1)
def get_members(curr_mod,curr_var,experiment_id):
df1=check_availability(curr_mod,curr_var,experiment_id)
return(np.unique(df1.member_id))
# In[5]:
def check_fixed_var_availability(curr_mod,fixed_var="sftlf",member_id="r1i1p1f1"):
ingrid_cmip6 = pd.read_csv("~/mary_cmip6.csv")
df1 = ingrid_cmip6[(ingrid_cmip6.source_id==curr_mod) & (ingrid_cmip6.variable_id==fixed_var) & (ingrid_cmip6.member_id == member_id)]
return(df1)
# In[6]:
def pull_fixedvar(curr_mod,curr_var,experiment_id,member_id,overwrite=False):
# Baseline directory
base_dir = 'http://mary.ldeo.columbia.edu:81/CMIP6/.'
# Write directory
base_write_dir= external_drive+'DROUGHT/fixedvar/'
df_proclist = pd.DataFrame(columns=['model','sim','ensemble','variable'])
ingrid_cmip6 = pd.read_csv("~/mary_cmip6.csv")
write_dir = base_write_dir
write_stem=curr_var+"_fx_"+curr_mod+".nc"
#write_stem = curr_var+"."+experiment_id+"."+curr_mod+"."+member_id+".*.nc"
if not overwrite:
already_exist=glob.glob(write_dir+write_stem)
if len(already_exist)!=0:
print("already exists")
#return False
#rips=np.unique(np.array(df1.member_id))
df1 = ingrid_cmip6[(ingrid_cmip6.source_id==curr_mod) & (ingrid_cmip6.variable_id==curr_var) & (ingrid_cmip6.experiment_id == experiment_id)& (ingrid_cmip6.member_id == member_id)]
#Construct openDAP link
nfiles,nidentifiers=df1.shape
if nfiles==1:
i_ens=0
nc_link = base_dir+df1.activity_id.iloc[i_ens]+'/.'+df1.institution_id.iloc[i_ens]+'/.'+curr_mod+'/.'+experiment_id+'/.'+df1.member_id.iloc[i_ens]+'/.'+df1.table_id.iloc[i_ens]+ '/.'+curr_var+'/.'+df1.grid_label.iloc[i_ens]+'/.'+df1.version.iloc[i_ens]+'/.'+df1.file_basename.iloc[i_ens]+'/.'+curr_var+'/dods'
request = requests.get(nc_link)
if request.status_code == 200:
#Get the data
f=cdms.open(nc_link)
data=f(curr_var)
fw=cdms.open(write_dir+write_stem,"w")
fw.write(data)
fw.close()
else:
print("more than one candidate found")
print(df1)
# In[7]:
def pull_land_fractions():
models=[x.split("/")[-1] for x in glob.glob(external_drive+"DROUGHT/DOWNLOADED_RAW/tas/*")]
already_done=[x.split("_fx_")[-1].split(".")[0] for x in glob.glob(external_drive+"DROUGHT/fixedvar/sftlf*")]
not_yet=np.setdiff1d(models,already_done)
for model in not_yet:
print(model)
# print(len(check_fixed_var_availability(model)))
if "piControl" in np.array(check_fixed_var_availability(model).experiment_id):
pull_fixedvar(model,"sftlf","piControl","r1i1p1f1")
elif "amip" in np.array(check_fixed_var_availability(model).experiment_id):
pull_fixedvar(model,"sftlf","amip","r1i1p1f1")
else:
print("nothing found for ",model)
# In[8]:
def download_hydrological_data(variables,experiments,models=None,verbose=False):
ingrid_cmip6 = pd.read_csv("~/mary_cmip6.csv")
if models is None:
models=np.unique(ingrid_cmip6.source_id)
for experiment_id in experiments:
for variable in variables:
for model in models:
rips=get_members(model,variable,experiment_id)
for rip in rips:
the_direc="/home/kdm2144/DROUGHT/DOWNLOADED_RAW/"+variable+"/"+model+"/"
num_already_downloaded=len(glob.glob(the_direc+"*"+experiment_id+"."+model+"."+rip+"*"))
if num_already_downloaded==0:
try:
downloaded=pull_data(model,variable,experiment_id,rip)
if verbose:
if downloaded:
print("model: ",model)
print("variable: ",variable)
print("experiment: ",experiment)
print("rip: ",rip)
except:
if verbose:
print("DID NOT DOWNLOAD:")
print("model: ",model)
print("variable: ",variable)
print("experiment: ",experiment_id)
print("rip: ",rip)
else:
if verbose:
print("Already downloaded!")
def NCA4_regions_average(variables,experiments,overwrite=False):
datadirec=external_drive+"DROUGHT/DOWNLOADED_RAW/"
writedirec=external_drive+"DROUGHT/NCA4/"
fixedvardirec=external_drive+"DROUGHT/fixedvar/"
###### LOOP OVER ALL VARIABLES #####
for variable in variables:
for region in NCA4regions.keys():
#cmd = "mkdir "+writedirec+"/"+region+"/"+variable
os.makedirs(writedirec+"/"+region+"/"+variable,exist_ok=True)
modeldirs=glob.glob(datadirec+variable+"/*")
### LOOP OVER ALL MODELS
for direc in modeldirs:
model=direc.split("/")[-1]
allfiles=glob.glob(direc+"/*"+variable+"*")
###### LOOP OVER ALL EXPERIMENTS #####
for experiment in experiments:
writedirecs={}
for region in NCA4regions.keys():
region_writedirec=writedirec+region+"/"+variable+"/"+experiment+"/"
cmd="mkdir "+region_writedirec
os.system(cmd)
writedirecs[region]=region_writedirec
allfiles_experiment=glob.glob(direc+"/"+variable+"."+experiment+".*")
rips=np.unique([x.split(".")[-3] for x in allfiles_experiment])
landthresh=1
#Get the land fraction
landfiles=glob.glob(fixedvardirec+"sftlf*"+model+".*")
if len(landfiles)==1:
fland=cdms.open(landfiles[0])
landfrac=fland("sftlf")
fland.close()
else:
print("can't find land fraction file for", model)
print(landfiles)
continue
#print(direc)
###### LOOP OVER ALL RIPS #####
for rip in rips:
#print(rip)
writenames={}
for key in NCA4regions.keys():
writename=writedirecs[key]+variable+"."+experiment+"."+model+"."+rip+".nc"
writenames[key]=writename
yearcheck=[]
ripfiles=np.sort(glob.glob(direc+"/"+variable+"."+experiment+"."+model+"."+rip+"*"))
L=len(ripfiles)
i=0
ripfile=ripfiles[i]
frip=cdms.open(ripfile)
data=frip(variable)
frip.close()
if data.shape[1:]!=landfrac.shape:
print("land mask wrong shape for "+variable+"."+experiment+"."+model+"."+rip)
continue
latax=landfrac.getLatitude()
lonax=landfrac.getLongitude()
tax=np.arange(12)
fpath,fexpt,fmodel,frip,fyear,fnc=ripfile.split(".")
###Loop over regions
for key in NCA4regions.keys():
if writenames[key] in glob.glob(writedirecs[key]+"*"):
if not overwrite:
continue
if key=="NW":
print(variable+"."+experiment+"."+model+"."+rip+"*")
region=NCA4regions[key]
DATA=MV.zeros(L*12)
landdata=cmip5.cdms_clone(np.repeat(.01*landfrac.asma()[np.newaxis],12,axis=0)*data,data)
landdata.setAxis(1,latax)
landdata.setAxis(2,lonax)
for i in range(L):
ripfile=ripfiles[i]
f=cdms.open(ripfile)
data=f(variable)
landdata=cmip5.cdms_clone(np.repeat(.01*landfrac.asma()[np.newaxis],12,axis=0)*data,data)
#Kludge since downloading process didn't preserve lat/lon designation
landdata.setAxis(1,latax)
landdata.setAxis(2,lonax)
f.close()
fpath,fexpt,fmodel,frip,fyear,fnc=ripfile.split(".")
DATA[12*i:12*(i+1)]=cdutil.averager(landdata(region),axis='xy')
yearcheck+=[float(fyear)]
tax=cdms.createAxis(np.arange(L*12))
tax.designateTime()
tax.units='months since '+str(yearcheck[0])+'-1-1'
if variable in ["pr","prsn","mrros","mrro","evspsbl"]:
DATA=DATA*60*60*24 #convert to mm day-1
DATA.units="mm day -1"
else:
DATA.units="kg m-2"
DATA.setAxis(0,tax)
DATA.id=variable
fw=cdms.open(writenames[key],"w")
fw.write(DATA)
fw.close()
#if key == "SE":
#print(variable+"."+experiment+"."+model+"."+rip+"*")
# In[ ]: