diff=diff[['date','mean','median','min','max','std']]
return diff
# Forth, the main program ############################################################################
for k in range(len(site)):
#################read-in obs data##################################
print site[k]
[lati,loni,bd]=getemolt_latlon(site[k]) # extracts lat/lon based on site code
#[lati,loni]=dm2dd(lati,loni)#converts decimal-minutes to decimal degrees
if surf_or_bott=='bott':
#dept=[bd[0]-0.25*bd[0],bd[0]+.25*bd[0]]
dept=[bd-0.25*bd,bd+.25*bd]
else:
dept=[0,5]
#(obs_dt,obs_temp,obs_salt,distinct_dep)=getobs_tempsalt(site[k], input_time=[dt(2006,1,10,1,0),dt(2013,12,31,0,0)], dep=dept)
(obs_dt,obs_temp,distinct_dep)=getobs_tempsalt(site[k],intime,dept)
#depthinfor.append(site[k]+','+str(bd[0])+','+str(distinct_dep)+'\n') # note that this distinct depth is actually the overall mean
depthinfor.append(site[k]+','+str(bd)+','+str(distinct_dep)+'\n') # note that this distinct depth is actually the overall mean
obs_dtindex=[]
if intend_to=='temp':
for kk in range(len(obs_temp)):
#obs_temp[kk]=f2c(obs_temp[kk]) # converts to Celcius
obs_dtindex.append(dt.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_temp,index=obs_dtindex)
else:
for kk in range(len(obs_salt)):
obs_dtindex.append(dt.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_salt,index=obs_dtindex)
print 'observed Dataframe is ready'
##################generate resample DataFrame and putput file################################################
if mode=='_post2007':
starttime_mod=dt.datetime(2008,1,1,0,0,0,0,pytz.UTC)
for k in range(len(site)):
#################read-in obs data##################################
print site[k]
#[lati,loni,on,bd]=getemolt_latlon(site[k]) # extracts lat/lon based on site code
[lati,loni,bd]=getemolt_latlon(site[k]) # extracts lat/lon based on site code
#[lati,loni]=dm2dd(lati,loni)#converts decimal-minutes to decimal degrees
if surf_or_bott=='bott':
#dept=[bd[0]-0.25*bd[0],bd[0]+.25*bd[0]]
dept=[bd-0.25*bd,bd+.25*bd]
else:
dept=[0,5]
#(obs_dt,obs_temp,obs_salt,distinct_dep)=getobs_tempsalt(site[k], input_time=[dt.datetime(1880,1,1),dt.datetime(2010,12,31)], dep=dept)
(obs_dt,obs_temp,distinct_dep)=getobs_tempsalt(site[k], input_time=[starttime_mod,endtime_mod], dep=dept)
#depthinfor.append(site[k]+','+str(bd[0])+','+str(distinct_dep)+'\n') # note that this distinct depth is actually the overall mean
depthinfor.append(site[k]+','+str(bd)+','+str(distinct_dep)+'\n') # note that this distinct depth is actually the overall mean
obs_dtindex=[]
if intend_to=='temp':
for kk in range(len(obs_temp)):
#obs_temp[kk]=f2c(obs_temp[kk]) # converts to Celcius
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_temp,index=obs_dtindex)
else:
for kk in range(len(obs_salt)):
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_salt,index=obs_dtindex)
print 'observed Dataframe is ready'
##################generate resample DataFrame and putput file################################################
#################read-in obs data##################################
print site[k]
[lati,loni,on,bd]=getemolt_latlon(site[k]) # extracts lat/lon based on site code
[lati,loni]=dm2dd(lati,loni)#converts decimal-minutes to decimal degrees
'''
[obs_dt,obs_temp]=getemolt_temp(site[k]) # extracts time series
obs_dtindex=[]
for kk in range(len(obs_temp)):
obs_temp[kk]=f2c(obs_temp[kk]) # converts to Celcius
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
'''
if surf_or_bott=='bott':
dept=[bd[0]-0.25*bd[0],bd[0]+.25*bd[0]]
else:
dept=[0,5]
(obs_dt,obs_temp,obs_salt,distinct_dep)=getobs_tempsalt(site[k], input_time=[dt.datetime(1880,1,1),dt.datetime(2010,12,31)], dep=dept)
depthinfor.append(site[k]+','+str(bd[0])+','+str(distinct_dep[0])+'\n')
obs_dtindex=[]
if intend_to=='temp':
for kk in range(len(obs_temp)):
obs_temp[kk]=f2c(obs_temp[kk]) # converts to Celcius
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_temp,index=obs_dtindex)
else:
for kk in range(len(obs_salt)):
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_salt,index=obs_dtindex)
print 'obs Dataframe is ready'
##################generate resample DataFrame and putput file################################################
reobsdaf=resamda(obstso)
#################read-in obs data##################################
print site[k]
[lati,loni,bd]=getemolt_latlon(site[k]) # extracts lat/lon based on site code
if surf_or_bott=='bott':
dept=[bd-0.25*bd,bd+.25*bd]
else:
dept=[0,5]
######################################################################################
if month_time<12:
input_time=[dt.datetime(2008,int(month_time),1,0,0,0,0,pytz.UTC),dt.datetime(2008,int(month_time)+1,1,0,0,0,0,pytz.UTC)]
else:
input_time=[dt.datetime(2008,int(month_time),1,0,0,0,0,pytz.UTC),dt.datetime(2008,int(month_time),31,0,0,0,0,pytz.UTC)]
dep=dept
print 'extracting eMOLT data using ERDDAP.. hold on'
(datet,temp,depths)=getobs_tempsalt(site[k],input_time)
obstso=pd.DataFrame(temp,index=datet)
print 'obs Dataframe is ready .. now getting model before assimilation'
if month_time<10:
month_time=str(0)+str(month_time)
urlbeforeassi='http://www.smast.umassd.edu:8080/thredds/dodsC/models/fvcom/NECOFS/Archive/eMOLT/gom'+str(month_time)+'_0001.nc'
nb = netCDF4.Dataset(urlbeforeassi)
nb.variables
latb = nb.variables['lat'][:]
lonb = nb.variables['lon'][:]
timesb = nb.variables['time']
jdb = netCDF4.num2date(timesb[:],timesb.units)
for kk in range(len(jdb)): # make these model times timezone aware
jdb[kk]=jdb[kk].replace(tzinfo=pytz.UTC)
varb = nb.variables[vname]
print 'Now find the index of space and time in the before assimilation model'
diff=diff[['date','mean','median','min','max','std']]
return diff
# Forth, the main program ############################################################################
for k in range(len(site)):
#################read-in obs data##################################
print site[k]
[lati,loni,bd]=getemolt_latlon(site[k]) # extracts lat/lon based on site code
#[lati,loni]=dm2dd(lati,loni)#converts decimal-minutes to decimal degrees
if surf_or_bott=='bott':
#dept=[bd[0]-0.25*bd[0],bd[0]+.25*bd[0]]
dept=[bd-0.25*bd,bd+.25*bd]
else:
dept=[0,5]
#(obs_dt,obs_temp,obs_salt,distinct_dep)=getobs_tempsalt(site[k], input_time=[dt(2006,1,10,1,0),dt(2013,12,31,0,0)], dep=dept)
(obs_dt,obs_temp,distinct_dep)=getobs_tempsalt(site[k], input_time=intime)
#depthinfor.append(site[k]+','+str(bd[0])+','+str(distinct_dep)+'\n') # note that this distinct depth is actually the overall mean
depthinfor.append(site[k]+','+str(bd)+','+str(distinct_dep)+'\n') # note that this distinct depth is actually the overall mean
obs_dtindex=[]
if intend_to=='temp':
for kk in range(len(obs_temp)):
#obs_temp[kk]=f2c(obs_temp[kk]) # converts to Celcius
obs_dtindex.append(dt.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_temp,index=obs_dtindex)
else:
for kk in range(len(obs_salt)):
obs_dtindex.append(dt.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_salt,index=obs_dtindex)
print 'observed Dataframe is ready'
##################generate resample DataFrame and putput file################################################
numperday=24
intend_to='temp'##############notice intend_to can be 'temp'or'salinity'
surf_or_bott='bott'
starttime=dt.datetime(2003,1,1)
endtime=dt.datetime(2010,12,31)
for k in range(len(site)):
#################read-in obs data##################################
print site[k]
[lati,loni,on,bd]=getemolt_latlon(site[k]) # extracts lat/lon based on site code
print bd
[lati,loni]=dm2dd(lati,loni)#converts decimal-minutes to decimal degrees
if surf_or_bott=='bott':
dept=[bd[0]-0.25*bd[0],bd[0]+.25*bd[0]]
else:
dept=[0,5]
(obs_dt,obs_temp,obs_salt,distinct_dep)=getobs_tempsalt(site[k], input_time=[starttime,endtime], dep=dept)
depthinfor.append(site[k]+','+str(bd[0])+','+str(distinct_dep[0])+'\n')
obs_dtindex=[]
if intend_to=='temp':
for kk in range(len(obs_temp)):
obs_temp[kk]=f2c(obs_temp[kk]) # converts to Celcius
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_temp,index=obs_dtindex)
else:
for kk in range(len(obs_salt)):
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_salt,index=obs_dtindex)
print 'obs Dataframe is ready'
resamdaobs=float64(obstso[0]).resample('A',how=['count','mean'])
resamdaobs.ix[resamdaobs['count']<7500,['mean']] = 'NaN'
dist2 = dx * dx + dy * dy
# dist1=np.abs(dx)+np.abs(dy)
i = np.argmin(dist2)
# min_dist=np.sqrt(dist2[i])
return i # ,min_dist
for k in range(len(site)):
#################read-in obs data##################################
print site[k][:]
[lati, loni, bd] = getemolt_latlon(site[k][:]) # extracts lat/lon based on site code
if surf_or_bott == "bott":
dept = [bd - 0.25 * bd, bd + 0.25 * bd] # use depth ranges within 25% of water column depth
else:
dept = [0, 5]
(obs_dt, obs_temp, depths) = getobs_tempsalt(site[k], input_time=[starttime, endtime], dep=dept)
# distinct_dep=unique(depths)
distinct_dep = list(set(depths))
depthinfor.append(site[k][:] + "," + str(bd) + "," + str(round(distinct_dep[0], 3)) + "\n")
obs_dtindex = []
for kk in range(len(obs_temp)):
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10], "%Y-%m-%d"))
obstso = pd.DataFrame(obs_temp, index=obs_dtindex)
print "obs Dataframe is ready"
resamdaobs = float64(obstso[0]).resample(
"A", how=["count", "mean"], loffset=td(days=-182.5)
) # need to offset because it uses the end of the averaging period as a timestamp by default
resamdaobs.ix[resamdaobs["count"] < 75, ["mean"]] = "NaN"
# the following makes a special results for cases where there is not a full year of data so , we plot this later with a bigger marker
resamdaobsdot = resamdaobs[(resamdaobs["count"] > 7500) & (resamdaobs["count"] < 8160)]
if modobs == "both":
print bd
[lati,loni]=dm2dd(lati,loni)#converts decimal-minutes to decimal degrees
print lati,loni
'''
[obs_dt,obs_temp]=getemolt_temp(site[k]) # extracts time series
obs_dtindex=[]
for kk in range(len(obs_temp)):
obs_temp[kk]=f2c(obs_temp[kk]) # converts to Celcius
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
'''
if surf_or_bott=='bott':
dept=[bd[0]-0.25*bd[0],bd[0]+.25*bd[0]]
#dept=[14,33]
else:
dept=[0,5]
(obs_dt,obs_temp,obs_salt,distinct_dep)=getobs_tempsalt(site[k], input_time=[dt.datetime(1880,1,1),dt.datetime(2010,12,31)], dep=dept)
depthinfor.append(site[k]+','+str(bd[0])+','+str(distinct_dep[0])+'\n')
obs_dtindex=[]
if intend_to=='temp':
for kk in range(len(obs_temp)):
obs_temp[kk]=f2c(obs_temp[kk]) # converts to Celcius
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_temp,index=obs_dtindex)
else:
for kk in range(len(obs_salt)):
obs_dtindex.append(datetime.strptime(str(obs_dt[kk])[:10],'%Y-%m-%d'))
obstso=pd.DataFrame(obs_salt,index=obs_dtindex)
print 'obs Dataframe is ready'
##################generate resample DataFrame and putput file################################################
reobsdaf=resamda(obstso)
dept = [0, 5]
# (obs_dt,obs_temp,obs_salt,distinct_dep)=getobs_tempsalt(site[k],
######################################################################################
if month_time < 12:
input_time = [
dt.datetime(2008, int(month_time), 1),
dt.datetime(2008,
int(month_time) + 1, 1)
]
else:
input_time = [
dt.datetime(2008, int(month_time), 1),
dt.datetime(2008, int(month_time), 31)
]
dep = dept
obs_dt, obs_temp, obs_salt, distinct_dep = getobs_tempsalt(
site[k], input_time, dep)
obs_dtindex = []
if intend_to == 'temp':
for kk in range(len(obs_temp)):
obs_temp[kk] = f2c(obs_temp[kk]) # converts to Celcius
obs_dtindex.append(
datetime.strptime(
str(obs_dt[kk])[:19], '%Y-%m-%d %H:%M:%S'))
obstso = pd.DataFrame(obs_temp, index=obs_dtindex)
else:
for kk in range(len(obs_salt)):
obs_dtindex.append(
datetime.strptime(
str(obs_dt[kk])[:19], '%Y-%m-%d %H:%M:%S'))
obstso = pd.DataFrame(obs_salt, index=obs_dtindex)
print 'obs Dataframe is ready'
