forked from TRACMASS/pytraj
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partsat.py
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partsat.py
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import os
import datetime
import glob
from datetime import datetime as dtm
from itertools import izip
import numpy as np
import pylab as pl
import scipy.io
from scipy.io import netcdf_file
from matplotlib.colors import LogNorm
import tables as td
from hitta import GBRY, WRY, GBW, GBW_R
import projmap, anim
import pytraj
import postgresql
import batch
miv = np.ma.masked_invalid
class Partsat(pytraj.Trm, postgresql.DB):
def __init__(self, projname, casename="", **kwargs):
super(Partsat,self).__init__(projname, casename, *kwargs)
postgresql.DB.__init__(self, projname, casename, database='partsat')
self.flddict = {'par':('L3',),'chl':('box8',)}
if projname == 'oscar':
import pysea.MODIS
self.sat = pysea.NASA.nasa(res='4km',
ijarea=(700,1700,2000,4000))
def calc_jd(ints,intstart):
return self.base_iso + float(ints)/6-1
elif projname=="casco":
self.sat = casco.Sat(res='500m')
def calc_jd(ints,intstart):
return (self.base_iso +(ints-(intstart)*10800)/150 +
intstart/8)
elif projname=="gompom":
n = netcdf_file('/Users/bror/svn/modtraj/box8_gompom.cdf')
self.gomi = n.variables['igompom'][:]
self.gomj = n.variables['jgompom'][:]
self.sati = n.variables['ibox8'][:]
self.satj = n.variables['jbox8'][:]
elif projname=="jplSCB":
from njord import mati
self.sat = mati.Cal()
elif projname=="jplNow":
from njord import mati
self.sat = mati.Cal()
def map2grid(self, vec):
""" Create map of average change in tracer """
fld = self.grid(vec)
cnt = self.grid()
return fld/cnt
def select(self,field="chl", jd=734107):
""" Get the interpolated tracer for all trajectories at jd=jd"""
dtype = np.dtype([('t1',np.float), ('t2',np.float),
('val1',np.float), ('val2',np.float),
('x',np.float), ('y',np.float)])
sql = """SELECT c.ints as t1, c2.ints as t2,
c.val as val1, c2.val as val2,
t.x, t.y FROM %s__%s c
INNER JOIN %s__%s c2 ON
c.runid=c2.runid AND c.ntrac=c2.ntrac
INNER JOIN %s t ON c.runid=t.runid AND c.ntrac=t.ntrac
WHERE c.ints > %i AND c.ints <= %i AND
c2.ints > %i AND c2.ints < %i AND t.ints=%i;"""
self.c.execute(sql % (self.tablename, field, self.tablename, field,
self.tablename, jd-10, jd, jd, jd+10, jd))
self.res = np.array(self.c.fetchall(), dtype=dtype)
if len(self.res)==0: return False
dt = (self.res['t2'] - self.res['t1'])
r1 = (self.res['t2'] - jd) / dt
r2 = (jd - self.res['t1']) / dt
setattr(self, field+'vec', self.res['val2']*r1 + self.res['val1'] * r2)
self.x = self.res['x']
self.y = self.res['y']
setattr(self, field, self.map2grid(getattr(self, field+'vec')))
return True
def sat_trajs(self,jd,field,pos='start'):
"""Retrive x-y pos and start-end values for trajs
Functionality replaced by 'select
'"""
if pos == 'start':
t1str = " AND t.ints=t1.ints "; t2str = ""
else:
t2str = " AND t.ints=t2.ints "; t1str = ""
table = self.tablename + '__' + field
self.c.execute(
"SELECT DISTINCT(ints) FROM %s WHERE runid=%i" %
(table,intstart) )
ints_t1t2 = self.c.fetchall()
if len(ints_t1t2) < 2:
self.empty = True
return
sql = """
SELECT t.x as x , t.y as y, t.ntrac as n,
t1.val as t1, t2.val as t2
FROM gompom t
INNER JOIN %s t1 ON
t.intstart=t1.intstart %s AND t.ntrac=t1.ntrac
INNER JOIN %s t2 ON
t.intstart=t2.intstart %s AND t.ntrac=t2.ntrac
WHERE t.intstart=%i AND t1.ints=%i AND t2.ints=%i
""" % (table, t1str, table, t2str, intstart,
ints_t1t2[0][0], ints_t1t2[1][0])
self.c.execute(sql)
res = zip(*self.c.fetchall())
if len(res) > 0:
for n,a in enumerate(['x','y','ntrac','t1','t2']):
self.__dict__[a] = np.array(res[n])
self.empty = False
else:
self.empty = True
self.ijll()
def sat_conc(self,intstart,field,pos='start'):
"""Retrive fields of start- and end-values"""
if not hasattr(self, jdS):
jdS = self.field_jds(field)
if intstart in jdS.t0:
if pos == 'start':
ints = jdS.t1[jdS.t0==intstart].item()
else:
ints = jdS.t2[jdS.t0==intstart].item()
else:
return self.llat * 0
table = self.tablename + field
sql = """
SELECT round(t.x) x, round(t.y) y, avg(n.val) val
FROM gompom t
INNER JOIN %s n ON
t.intstart=n.intstart AND t.ntrac=n.ntrac
WHERE t.intstart=%i AND n.ints=%s AND t.ints=%s
GROUP BY round(t.x),round(t.y)
""" % (table, intstart, intstart+1, ints)
n = self.c.execute(sql)
fld = self.llat * 0
if n == 0: return fld
x,y,val = zip(*self.c.fetchall())
fld[np.array(y).astype(int),np.array(x).astype(int)-1] = val
return fld
def trajs(self,jdstart=0, jd=0, ntrac=0, fld=''):
""" Retrive trajectories from database """
whstr = ""
if jd != 0:
pass
#whstr += " t.runid = %i AND" % runid
if jd != 0:
whstr += " t.ints = %i AND" % jd
if ntrac != 0:
whstr += " t.ntrac = %i " % ntrac
whstr = whstr.rstrip("AND")
if fld:
valstr = " ,n.val val "
table2 = self.tablename + "__" + fld
valwhere = " AND n.ints=%s " % jd
valjoin = (" INNER JOIN %s n ON " % table2 +
" t.runid=n.runid AND t.ntrac=n.ntrac ")
else:
valstr = " ,t.runid val "
table2 = valwhere = valjoin = ''
sql = ("""SELECT t.ints ints, t.ntrac ntrac, t.x x, t.y y
%s FROM %s t %s WHERE %s %s """
% (valstr, self.tablename, valjoin, whstr, valwhere) )
n = self.c.execute(sql)
res = zip(*self.c.fetchall())
if len(res) > 0:
for n,a in enumerate(['ints','ntrac','x','y','val']):
self.__dict__[a] = np.array(res[n])
#self.ijll()
def get_satdata(self,field, jd=0):
""" Load the satellite field corresponding to a given jd.
Generate a vector with satellite data at all particle
positions currently in memory """
self.sat.load(field,jd=jd)
self.ijll()
self.sati,self.satj = self.sat.ll2ij(self.lon,self.lat)
self.__dict__[field] = self.sat.__dict__[field][self.sati,self.satj]
def create_fieldtable(self,field):
"""Create a postgresql table for satellite fields """
tablename = self.tablename + '__' + field
if self.table_exists(tablename): return
sql = "CREATE TABLE %s (runid INT, ints FLOAT8, ntrac INT ,val REAL )"
self.c.execute(sql % tablename)
self.conn.commit()
def insert_sat_to_db(self,field,jd1,jd2=None):
"""Insert field data into a table. """
self.create_fieldtable(field)
def insertload(jd):
self.select(ints=jd)
if not hasattr(self,'x'): return
self.get_satdata(field,jd=jd)
mask = ~np.isnan(self.__dict__[field])
plist = zip(self.runid[mask], self.ints[mask], self.ntrac[mask],
self.__dict__[field][mask])
tablename = self.tablename + '__' + field
sql = ("INSERT INTO " + tablename + " (runid,ints,ntrac,val) " +
" VALUES (%s,%s,%s,%s)")
self.c.executemany(sql,plist)
self.conn.commit()
if not jd2:
insertload(jd1)
else:
for jd in np.arange(jd1,jd2+1):
print jd2-jd
insertload(jd)
batch.purge()
def field_jds(self,field):
table = self.tablename + '__' + field
class ints: pass
self.c.execute("SELECT runid, min(ints), max(ints) " +
"FROM %s GROUP BY runid" % table)
res = zip(*self.c.fetchall())
if len(res) > 0:
for n,a in enumerate(['t0','t1','t2']):
ints.__dict__[a] = np.array(res[n])
return ints
def median_ncp(self):
class svec: pass
self.c.execute(
"""CREATE TEMPORARY TABLE IF NOT EXISTS temp_median
(id INT AUTO_INCREMENT PRIMARY KEY)
SELECT ints, val FROM gompomncp
ORDER BY ints, val;
""")
self.c.execute(
"""CREATE TEMPORARY TABLE temp_median_ids
SELECT ROUND(AVG(id)) AS id FROM temp_median
GROUP BY ints;
""")
self.c.execute(
"""SELECT ints, val FROM temp_median_ids
LEFT JOIN temp_median USING (id) ORDER BY ints;
""")
res = zip(*self.c.fetchall())
svec.t1 = res[0]
svec.medianNCP = res[1]
self.c.execute(
"""SELECT ints, count(val),max(val),min(val),avg(val)
FROM gompomncp GROUP BY ints
ORDER BY ints;
""")
res = zip(*self.c.fetchall())
svec.sum = res[1]
svec.max = res[2]
svec.min = res[3]
svec.mean = res[4]
return svec
def calc__z_eu(self, chl):
""" Calculate euphotic depth from chl with Morel's Case I model"""
chl_tot = 40.2 * chl**0.507
mask = chl < 1
chl_tot[mask] = 38.0 * chl[mask]**0.425
z_eu = 200.0 * chl_tot**-0.293
mask = z_eu <= 102.0
z_eu[mask] = 568.2 * chl_tot[mask]**-0.746
return z_eu
class DeltaField(Partsat):
""" Calculate change in tracer from one day to another """
def __init__(self,projname, casename="", **kwargs):
Partsat.__init__(self,projname,casename,**kwargs)
self.jdmin = 733773.25
self.jdmax = 734137.75
self.djd = 1.
self.h5dir = "./"
def select(self,field="Dchl", jd=734107):
""" Get the change in tracer for all trajectories at jd=jd"""
if field[0] is not "D":
return super(DeltaField,self).select(field, jd)
field = field[1:]
dtype = np.dtype([('dt',np.float),('val1',np.float),('val2',np.float),
('x',np.float), ('y',np.float)])
sql = """SELECT c2.ints-c.ints as dt, c.val as val1, c2.val as val2,
t.x, t.y FROM %s__%s c
INNER JOIN %s__%s c2 ON
c.runid=c2.runid AND c.ntrac=c2.ntrac
INNER JOIN %s t ON c.runid=t.runid AND c.ntrac=t.ntrac
WHERE c.ints > %i AND c.ints <= %i AND
c2.ints > %i AND c2.ints < %i AND t.ints=%i;"""
self.c.execute(sql % (self.tablename, field, self.tablename, field,
self.tablename, jd-10, jd, jd, jd+10, jd))
self.res = np.array(self.c.fetchall(), dtype=dtype)
if len(self.res)==0: return False
field = 'D' + field
setattr(self, field + 'vec', (self.res['val2'] - self.res['val1']) /
self.res['dt'])
self.x = self.res['x']
self.y = self.res['y']
setattr(self, field, self.map2grid(getattr(self, field + 'vec')))
return True
def get_ncp(self, jd=734107):
""" get NCP from database"""
if self.select('Dchl', jd=jd):
z_eu = self.calc__z_eu(self.res['val1'])
self.ncpvec = self.Dchlvec * z_eu * 71.3 / 12
self.ncpvec[(self.ncpvec<-5000) | (self.ncpvec>5000)] = np.nan
self.ncp = self.map2grid(self.ncpvec)
def pcolor(self, field, jd=None, vmin=-10,vmax=10, oneside=False,
clf=True, cb=True, ):
"""Plot a map of a field using projmap"""
self.add_mp()
if clf:
pl.clf()
pl.subplot(111,axisbg='0.9')
if (oneside==True) | (oneside=="pos"):
cmap = WRY()
elif oneside=="neg":
cmap = GBW()
elif oneside=="negr":
cmap = GBW_R()
else:
cmap = GBRY()
self.mp.pcolormesh(self.mpxll,self.mpyll,miv(field), rasterized=True,
cmap=cmap, vmin=vmin,vmax=vmax)
self.mp.nice(latlabels=False, lonlabels=False)
if jd: pl.title(pl.num2date(jd).strftime("%Y-%m-%d"))
if type(cb) is dict:
pl.colorbar(aspect=40,shrink=0.95,pad=0,fraction=0.05, **cb)
elif cb:
pl.colorbar(aspect=40,shrink=0.95,pad=0,fraction=0.05)
def movie(self, jd1, jd2, field='chl'):
"""Create a movie of the daily changes in tracer"""
mv = anim.Movie()
for jd in np.arange(jd1,jd2+1):
t1 = dtm.now()
print "Images left: ", jd2-jd
self.map2grid(field=field, jd=jd)
self.pcolor(self.dfld, jd)
mv.image()
print "Delta time: ", dtm.now() - t1
mv.video(self.projname + "_" + field + "_mov.mp4",r=2)
def h5array(self, field='Dchl'):
"""Create a tables array with gridded daily tracer changes"""
self.h5open(field)
for n,jd in enumerate(np.arange(self.jdmin, self.jdmax+1)):
t1 = dtm.now()
print "Images left: ", self.jdmax-jd
if self.select(field=field, jd=jd):
tpos = np.nonzero(self.h5f.root.jdvec==jd)[0][0]
self.h5field[tpos,:,:] = getattr(self, field)
print tpos
print "Delta time: ", dtm.now() - t1
self.h5close()
def h5open(self, field):
self.h5filename = os.path.join(self.h5dir,"partsat_%s_%s.h5" %
(self.projname, self.casename))
self.h5f = h5f = td.openFile(self.h5filename, 'a')
jdvec = int((self.jdmax-self.jdmin+1)/self.djd)+1
shape = (jdvec, self.jmt, self.imt)
fatom = td.FloatCol()
filtr = td.Filters(complevel=5, complib='zlib')
crc = h5f.createCArray
if not hasattr(h5f.root, 'jdvec'):
jdvec = crc(h5f.root, 'jdvec', fatom, (shape[0],))
jdvec[:] = np.arange(self.jdmin, self.jdmax+1, self.djd)
if not hasattr(h5f.root, field):
fieldmat = crc(h5f.root, field, fatom, shape, filters=filtr)
self.h5field = self.h5f.root._f_getChild(field)
def h5close(self):
if not hasattr(self, 'h5f'): return
self.h5f.close()
del self.h5f
del self.h5filename
def histmoeller(self,fldname):
"""Create a hofmoeller representation of diff distributions"""
sql = "SELECT min(ints),max(ints) FROM jplnowfull;"
self.c.execute(sql)
[jdmin,jdmax] = self.c.fetchall()[0]
self.jdvec = np.arange(jdmin,jdmax)
self.hpos = np.linspace(0, 100, 100)
self.posmat = np.zeros((len(jdvec), len(self.hpos)-1))
self.negmat = np.zeros((len(jdvec), len(self.hpos)-1))
for n,jd in enumerate(self.jdvec):
if not self.select(fldname,jd=jd): continue
diff = (self.val2-self.val1)/self.dt
self.negmat[n,:],_ = np.histogram(-diff[diff<0], self.hpos)
self.posmat[n,:],_ = np.histogram(diff[diff>0], self.hpos)
print n,jd,len(-diff[diff<0]),len(diff[diff>0])
def cumcum_moeller(self,fldname):
"""Create a cumcum representation of diff distributions"""
sql = "SELECT min(ints),max(ints) FROM jplnowfull;"
self.c.execute(sql)
[jdmin,jdmax] = self.c.fetchall()[0]
jdvec = np.arange(jdmin,jdmax)
self.poscum = np.zeros((len(jdvec), 100))
self.negcum = np.zeros((len(jdvec), 100))
self.pavglog =[]
self.pavglin =[]
self.navglog =[]
self.navglin =[]
for n,jd in enumerate(jdvec):
if not self.select(fldname,jd=jd): continue
_,self.negcum[n,:] = self.cumcum(-self.diff[self.diff<0])
_,self.poscum[n,:] = self.cumcum(self.diff[self.diff>0])
print n,jd,len(self.diff)
self.pavglog.append(np.mean(np.log(self.diff[self.diff>0])))
self.pavglin.append(np.mean(self.diff[self.diff>0]))
self.navglog.append(np.mean(np.log(-self.diff[self.diff<0])))
self.navglin.append(np.mean(-self.diff[self.diff<0]))
def cumcum(self, vec):
"""Create a interpolated 'hypsograph' from a vector"""
vec = vec[~np.isnan(vec)]
xi = np.linspace(0,1,100)
xvec = np.arange(len(vec)).astype(np.float)/len(vec)
yvec = np.cumsum(np.sort(vec)[::-1].astype(np.float)/sum(vec))
yi = np.interp(xi, xvec, yvec)
return xi,yi
def cumpos(self, vec, pos=0.1):
""" Blaaaa"""
vec = vec[~np.isnan(vec)]
xvec = np.arange(len(vec)).astype(np.float)/len(vec)
yvec = np.cumsum(np.sort(vec)[::-1].astype(np.float)/sum(vec))
return np.interp(pos, xvec, yvec)
def cumcumplot(self, fldname, jd=734107):
"""Plot a 'hypsograph of the relative distribution of diffs"""
self.select(fldname, jd=jd)
pl.clf()
pl.plot(*self.cumcum(abs(np.random.rand(500000))))
pl.plot(*self.cumcum(abs(np.random.randn(500000))))
pl.plot(*self.cumcum(-self.diff[self.diff<0]))
pl.plot(*self.cumcum( self.diff[self.diff>0]))
pl.xlim(0,1)
pl.ylim(0,1)
pl.title("%s %s" % (fldname, pl.num2date(jd).strftime('%Y-%m-%d')))
pl.legend(('Random values, symetrical', 'Random values, gaussian',
'Trajs, increasing values','Trajs, decreasing values'))
pl.savefig('figs/cumcumplot_%s_%i.pdf' % (fldname, jd))
#####################################################################
def ncp(t, db=False,kpar=False):
class tr: pass
t = int(t)
tr_chl = traj('gompom')
tr_chl.sat_trajs(t, 'chlor_a')
tr_chl2 = traj('gompom')
tr_chl2.sat_trajs(t, 'chlor_a', pos='end')
if tr_chl.empty: return False
tr_k49 = traj('gompom')
tr_k49.sat_trajs(t, 'K_490')
if kpar:
"""
http://oceancolor.gsfc.nasa.gov/forum/oceancolor/topic_show.pl?tid=2997
"""
tr_k49.t1 = 0.0864 + 0.884*tr_k49.t1 - 0.00137/tr_k49.t1
tr_k49.t2 = 0.0864 + 0.884*tr_k49.t2 - 0.00137/tr_k49.t2
msk = np.intersect1d(tr_chl.ntrac,tr_k49.ntrac)
tr_k49.msk = [np.flatnonzero(tr_k49.ntrac==m).item()
for m in msk]
tr_chl.msk = [np.flatnonzero(tr_chl.ntrac==m).item()
for m in msk]
tr_k49.eu1 = float(4.6) / tr_k49.t1
tr_k49.eu2 = float(4.6) / tr_k49.t2
if len(tr_chl.msk) == 0: return False
jds =tr_chl.field_jds('chlor_a')
tr.pc2 = tr_chl.t2[tr_chl.msk] * tr_k49.eu2[tr_k49.msk]*60.
tr.pc1 = tr_chl.t1[tr_chl.msk] * tr_k49.eu1[tr_k49.msk]*60.
dt = (jds.t2[jds.t0==t]/6. - jds.t1[jds.t0==t]/6.).item()
tr.ncp = (tr.pc2-tr.pc1) / dt
tr.ntrac = tr_chl.ntrac[tr_chl.msk]
tr.x1 = tr_chl.x[tr_chl.msk]
tr.y1 = tr_chl.y[tr_chl.msk]
tr.x2 = tr_chl2.x[tr_chl.msk]
tr.y2 = tr_chl2.y[tr_chl.msk]
tr.lon1 = tr_chl.lon[tr_chl.msk]
tr.lat1 = tr_chl.lat[tr_chl.msk]
tr.lon2 = tr_chl2.lon[tr_chl.msk]
tr.lat2 = tr_chl2.lat[tr_chl.msk]
if db:
tr.ints=tr.x1*0+t
tr.val = tr.ncp
tr_chl.sat_to_db('ncp',intstart=t,ints=t,batch=False,traj=tr)
return tr
def batch_sat_to_db(field='chlor_a',batchprefix='batch_ints'):
tr = traj('gompom')
file1 = tr.ormdir + "/projects/gomoos/" + batchprefix + "_start.asc"
file2 = tr.ormdir + "/projects/gomoos/" + batchprefix + "_end.asc"
for t1,t2 in zip(open(file1),open(file2)):
print int(t1), int(t1)+1, int(t1) ,int(t2), int(t2)-int(t1)
if int(t1) > 4000:
try:
tr.sat_to_db(field,int(t1),int(t1)+1,batch=True)
except IOError:
print "*** File missing! ***"
try:
tr.sat_to_db(field,int(t1),int(t2),batch=True)
except IOError:
print "*** File missing! ***"
tr.enable_indexes()
def calc_ncp_time(batchfile='batch_ints_start.asc'):
import partsat
class tS: pass
tr = partsat.traj('gompom')
tS.ncp = []
tS.iso = []
tS.cnt = []
for t in open(tr.ormdir + "/projects/gomoos/" + batchfile):
ncp = tr.traj_ncp(int(t))
if not ncp: continue
tS.ncp.append(np.median(ncp.ncp))
tS.iso.append(tr.ints2iso(int(t)))
tS.cnt.append(len(ncp.ncp))
return tS
def interp(ps,intstart=6353):
import figpref
import projmaps
figpref.current()
ps.c.execute("SELECT distinct(ints) FROM casco__chlor_a " +
" WHERE intstart=%i" % intstart)
ints1,ints2 = ps.c.fetchall()
sql = """SELECT t1.ntrac,t1.val,t2.val,p.x,p.y,p.ints
FROM casco__chlor_a t1
INNER JOIN casco__chlor_a t2 ON t1.ntrac=t2.ntrac
INNER JOIN casco p ON t1.ntrac=p.ntrac
WHERE t1.intstart=%i
AND t2.intstart=%i
AND p.intstart=%i
AND t1.ints=%i
AND t2.ints=%i;
""" % (intstart,intstart,intstart,ints1[0],ints2[0])
ps.c.execute(sql)
res = zip(*ps.c.fetchall())
class trj: pass
if len(res) > 0:
for n,a in enumerate(['ntrac','t1val','t2val','x','y','ints']):
trj.__dict__[a] = np.array(res[n])
mask = (trj.ints.astype(np.float)/100-trj.ints/100)>0.5
trj.ints[mask]=trj.ints[mask]+1
tvec = np.unique(trj.ints)
tvec = tvec[tvec<=ints2[0]]
itvec = tvec-tvec.min()
itvec = itvec.astype(np.float)/itvec.max()
mp = projmaps.Projmap('casco')
xl,yl = mp(ps.llon,ps.llat)
for n,t in enumerate(tvec):
fld = ps.cs.llat * 0
cnt = ps.cs.llat * 0
xvc = (trj.x[trj.ints==t].astype(np.int))
yvc = (trj.y[trj.ints==t].astype(np.int))
val = ( np.log(trj.t1val[trj.ints==t])*(1-itvec[n]) +
np.log(trj.t2val[trj.ints==t])*(itvec[n]) )
for x,y,v in zip(xvc,yvc,val):
fld[y,x] += v
cnt[y,x] += 1
pl.clf()
mp.pcolormesh(xl,yl,miv(fld/cnt))
mp.nice()
jd = (ps.base_iso +
(float(t)-(intstart)/8*3600*24)/3600/24 + intstart/8) + 0.583
pl.title(pl.num2date(jd).strftime("log(Chl) %Y-%m-%d %H:%M"))
pl.clim(-2,2.5)
pl.savefig("interp_%i_%03i.png" % (intstart,n), dpi=100)
def batch_insert():
import batch
def copy(jd):
tr = traj('jplNOW','ftp','/Volumes/keronHD3/ormOut/')
print pl.num2date(jd), jd
tr.load(jd)
tr.remove_satnans()
if len(tr.x>0):
tr.db_copy()
#batch.jdloop(copy,733773.0, 734138.0,3)
for jd in np.arange(733865.0,734138):
dt1 = pl.date2num(dtm.now())
copy(jd)
dt2 = pl.date2num(dtm.now())
print "----------",dt2-dt1
def profile():
import cProfile
cProfile.run('test()')