freqs = oldFreqs
freqs=10.
print 'Reading input file ',sys.argv[1]
j = planet.planet('jupiter')
gasFile = 1
cloudFile = 1
if len(sys.argv) > 2:
cloudFile = 2
j.atm.readGas(sys.argv[gasFile])
j.atm.readCloud(sys.argv[cloudFile])
j.atm.tweakAtm()
j.atm.computeProp()
regrid.regrid(j.atm,regridType=j.atm.config.regridType,Pmin=j.atm.config.pmin,Pmax=j.atm.config.pmax)
j.atm.nAtm=len(j.atm.gas[0])
b = [90.,0.0,0.2,0.4,0.6,0.7,0.8,0.9]
j.run(freqs,b=b,outputType='batch')
nfreq,nTB = splitFile()
shutil.copy('specoutputline.dat','limb.dat')
j.run('reuse','disc',outputType='batch')
dfreq,dTB = splitFile()
fp = open('runpResults.dat','a')
fn = sys.argv[1].split('/')
s = fn[-1]+'\t'
fp.write(s)
s = ''
def subtract(flux1, time1, cadence, q, factor, cfilepath, centroid_type,
cutoutdims, cluster, isolation):
workingdir = os.getcwd()
if centroid_type == 'e':
os.chdir(cfilepath)
centroids = np.loadtxt(f'{cluster}q{q}centroid.dat', delimiter=',')
os.chdir(workingdir)
x_shifts = centroids[:, 0]
y_shifts = centroids[:, 1]
# make mask first before cutting down flux
mask = gaussmask(factor, flux1.shape[1], flux1.shape[2], isolation)
# cut down both mask and flux, i.e. get rid of zeros
flux1, mask = zerocutter(flux1, mask, factor)
# prepare arrays/get dimensions etc
if centroid_type == 'e':
fluxnew, timenew, x_shifts, y_shifts = nc.nancleaner3d_c(
flux1, time1, x_shifts, y_shifts)
elif centroid_type == 't':
fluxnew, timenew = nc.nancleaner3d(flux1, time1)
dump, newcadence = nc.nancleaner3d(flux1, cadence)
timespan = fluxnew.shape[0]
# regridding
oldy, oldx = fluxnew.shape[1], fluxnew.shape[2] # reset dims
newy, newx = fluxnew.shape[1] * factor, fluxnew.shape[2] * factor
re_flux = np.zeros((timespan, newy, newx))
shifted = np.zeros((timespan, newy, newx))
nanmask = np.asarray(np.where(np.isnan(fluxnew) == True))
rg_nanmask = np.array(
(nanmask[0], nanmask[1] * factor, nanmask[2] * factor))
for i in range(timespan):
interpolant = spi.RectBivariateSpline(np.arange(oldy),
np.arange(oldx),
np.nan_to_num(fluxnew[i]),
kx=1,
ky=1)
re_flux[i] = interpolant(np.linspace(0, oldy - 1, newy),
np.linspace(0, oldx - 1,
newx)) #/ (factor*factor)
# regridding base cutout for plotting (oy vey)
avg_original = np.nanmean(fluxnew, axis=0)
regridded_base = rg.regrid(avg_original, factor)
# centroiding
originalmask = np.zeros(flux1[0].shape) #+3
originalmask[cutoutdims - 2:cutoutdims + 1,
cutoutdims - 2:cutoutdims + 1] += 3 # 3x3 box for checking
mask_rg = rg.regrid_slow(originalmask, factor)
if centroid_type == 't':
x_cent, y_cent = ct.centroid(timespan, re_flux, mask_rg)
midx, midy = (newx - 1) / 2, (newy - 1) / 2
x_shifts = (x_cent - midx)
y_shifts = (y_cent - midy)
elif centroid_type == 'e':
x_cent = 0
y_cent = 0
for i in range(timespan):
interpolant = spi.RectBivariateSpline(np.arange(newy),
np.arange(newx),
re_flux[i, :, :],
kx=1,
ky=1)
# interpolant = spi.interp2d(np.arange(newy), np.arange(newx), re_flux[i,:,:], kind='cubic')
shifted[i, :, :] = interpolant(
np.linspace(0 - y_shifts[i], newy - y_shifts[i], newy),
np.linspace(0 - x_shifts[i], newx - x_shifts[i], newx))
avgshift = np.nanmean(shifted, axis=0)
# mask making
maskrange = np.nanmax(avgshift) - np.nanmin(avgshift)
# maskmax = np.nanmax(avgshift) - 0.5 * maskrange
maskmin = np.nanmin(avgshift) + 0.05 * maskrange # cut for background
# defining six different standard masks
# maskarr = np.zeros((6,newy,newx))
# maskarr[0][(cutoutdims-1)*factor:cutoutdims*factor,(cutoutdims-1)*factor:cutoutdims*factor] +=1
# maskarr[1][(cutoutdims-2)*factor:(cutoutdims+1)*factor,(cutoutdims-1)*factor:cutoutdims*factor] += 1
# maskarr[1][(cutoutdims-1)*factor:cutoutdims*factor,(cutoutdims-2)*factor:(cutoutdims-1)*factor] += 1
# maskarr[1][(cutoutdims-1)*factor:cutoutdims*factor,cutoutdims*factor:(cutoutdims+1)*factor] += 1
# maskarr[2] += 1
# maskarr[3] += circle(9, factor, oldy, oldx)
# maskarr[4] += circle(5, factor, oldy, oldx)
# maskarr[5] += circle(3, factor, oldy, oldx)
# if mask != None:
# maskarr = np.delete(maskarr, np.delete(np.arange(6), mask), axis=0)
# annulus
mask_bg = np.zeros((newy, newx))
counter = 0
for index, val in np.ndenumerate(avgshift):
if avgshift[index] < maskmin: # and np.isnan(avgshift[index]) == False:
mask_bg[index] = -1
counter += 1
# calculating background
lclength = len(timenew)
for j in range(lclength):
bg = np.nansum(shifted[j][np.where(mask_bg == -1)])
bg /= counter
shifted[j] -= bg
avgflux = np.nanmean(shifted, axis=0) # new average image after
output = np.c_[newcadence, timenew]
new_flux = np.zeros(lclength)
ap_av = np.nansum(avgflux * mask)
for j in range(lclength):
placeholder = shifted[j] - avgflux
aperture = np.nansum(placeholder * mask)
# annulus = sum(placeholder[np.where(mask==-1)])
# annulus /= counter
new_flux[j] = aperture + ap_av #- annulus
output = np.c_[output, new_flux]
# for deprecated version with multiple masks:
# for i in range(maskarr.shape[0]):
# new_flux = np.zeros(lclength)
# ap_av = np.nansum(avgflux[np.where(maskarr[i]==1)])
# for j in range(lclength):
# placeholder = shifted[j] - avgflux
# aperture = np.nansum(placeholder[np.where(maskarr[i]==1)])
# # annulus = sum(placeholder[np.where(mask==-1)])
# # annulus /= counter
# new_flux[j] = aperture + ap_av #- annulus
# output = np.c_[output, new_flux]
return output, mask, avgflux, regridded_base, x_cent, y_cent # prev maskarr
dset_nb_days['thresh'] = ('thresh', np.array([0, 1, 5]))
dset_nb_days['nbdays'] = (('thresh','lat','lon'), arr_nb)
dset_nb_days = xr.Dataset(dset_nb_days)
# dset_nb_days.sel(thresh=5)['nbdays'].plot()
new_res = 5
new_lon = np.linspace(lon[0], lon[-1], len(lon) * new_res, endpoint=True)
new_lat = np.linspace(lat[0], lat[-1], len(lat) * new_res, endpoint=True)
dset_30_days.load()
new_lon, new_lat, matm = regrid(dset_30_days.sum('time')['trmm'].data, lon, lat, new_lon, new_lat)
lons, lats = np.meshgrid(new_lon, new_lat)
sys.path.append(os.path.join(os.environ['HOME'], 'pythonlibs'))
import nclcmaps as ncm
cmap = ncm.cmap('GMT_drywet')
proj = ccrs.PlateCarree()
f, ax = plt.subplots(figsize=(10, 8), subplot_kw=dict(projection=proj))
ax.set_extent([domain['lonmin'], domain['lonmax'], domain['latmin'], domain['latmax']])
def sapper(flux1, time1, cadence, q, factor, cutoutdims, isolation):
workingdir = os.getcwd()
# regridding
fluxnew, timenew = nc.nancleaner3d(flux1, time1)
# make mask first before cutting down flux
mask = gaussmask(factor, flux1.shape[1], flux1.shape[2], isolation)
# cut down both mask and flux, i.e. get rid of zeros
flux1, mask = zerocutter(flux1, mask, factor)
dump, newcadence = nc.nancleaner3d(flux1, cadence)
timespan = fluxnew.shape[0]
oldy, oldx = fluxnew.shape[1], fluxnew.shape[2]
newy, newx = fluxnew.shape[1] * factor, fluxnew.shape[2] * factor
re_flux = np.zeros((timespan, newy, newx))
nanmask = np.asarray(np.where(np.isnan(fluxnew) == True))
rg_nanmask = np.array(
(nanmask[0], nanmask[1] * factor, nanmask[2] * factor))
for i in range(timespan):
re_flux[i] = rg.regrid(fluxnew[i], factor)
# regridding base cutout for plotting (oy vey)
avg_original = np.mean(fluxnew, axis=0)
regridded_base = rg.regrid(avg_original, factor)
avgflux = np.mean(re_flux, axis=0)
# mask making
maskrange = np.nanmax(avgflux) - np.nanmin(avgflux)
maskmin = np.nanmin(avgflux) + 0.05 * maskrange # cut for background
# annulus
mask_bg = np.zeros((newy, newx))
counter = 0
for index, val in np.ndenumerate(avgflux):
if avgflux[index] < maskmin: # and np.isnan(avgshift[index]) == False:
mask_bg[index] = -1
counter += 1
# calculating background
lclength = len(timenew)
for j in range(lclength):
bg = np.nansum(re_flux[j][np.where(mask_bg == -1)])
bg /= counter
re_flux[j] -= bg
avgflux = np.mean(re_flux, axis=0) # new average image after
output = np.c_[newcadence, timenew]
ap_av = np.nansum(avgflux * mask)
new_flux = np.zeros(lclength)
for j in range(lclength):
placeholder = re_flux[j] - avgflux
aperture = np.nansum(placeholder * mask)
# annulus = sum(placeholder[np.where(mask==-1)])
# annulus /= counter
new_flux[j] = aperture + ap_av #- annulus
output = np.c_[output, new_flux]
return output, mask, avgflux, regridded_base
freqs = oldFreqs
freqs = 10.
print 'Reading input file ', sys.argv[1]
j = planet.planet('jupiter')
gasFile = 1
cloudFile = 1
if len(sys.argv) > 2:
cloudFile = 2
j.atm.readGas(sys.argv[gasFile])
j.atm.readCloud(sys.argv[cloudFile])
j.atm.tweakAtm()
j.atm.computeProp()
regrid.regrid(j.atm,
regridType=j.atm.config.regridType,
Pmin=j.atm.config.pmin,
Pmax=j.atm.config.pmax)
j.atm.nAtm = len(j.atm.gas[0])
b = '0.0,0.2,0.4,0.6,0.7,0.8,0.9<90'
j.run(freqs, b=b, outputType='batch')
nfreq, nTB = splitFile()
shutil.copy('specoutputline.dat', 'limb.dat')
j.run('reuse', 'disc', outputType='batch')
dfreq, dTB = splitFile()
fp = open('runpResults.dat', 'a')
fn = sys.argv[1].split('/')
s = fn[-1] + '\t'
fp.write(s)
def run(self,Pmin=None,Pmax=None,regridType=None,gasType=None,cloudType=None,otherType=None,tweak=True,plot=None,verbose=None):
"""This is the standard pipeline"""
###Set run defaults
if Pmin == None:
Pmin = self.config.pmin
if Pmax == None:
Pmax = self.config.pmax
if regridType == None:
regridType = self.config.regridType
if verbose==None:
verbose = self.verbose
if plot==None:
plot = self.plot
if gasType==None:
gasType = self.config.gasType
if cloudType==None:
cloudType = self.config.cloudType
if otherType==None:
otherType = self.config.otherType
self.nAtm = 0
### Generate gas profile (gasType is 'read' or 'compute')
if not self.gasGen.has_key(gasType):
print 'Error: No such gasType: '+gasType
return 0
else:
self.gasGen[gasType](verbose=verbose)
if not self.batch:
### Generate cloud profile (cloudType is 'read' or 'compute')
if not self.cloudGen.has_key(cloudType):
print 'Error: No such cloudType: '+cloudType
return 0
else:
self.cloudGen[cloudType](verbose=verbose)
if tweak: # This loads and calls the module 'tweakFile'
self.tweakAtm()
### Compute other parameters that are needed
if not self.propGen.has_key(self.config.otherType):
print 'Error: no such otherTpe: '+otherType
return 0
else:
self.propGen[otherType](verbose=verbose)
### Put onto common grid
regridded = regrid.regrid(self,regridType=regridType,Pmin=Pmin,Pmax=Pmax)
self.nAtm = len(self.gas[0])
angularDiameter = 2.0*math.atan(self.layerProperty[self.config.LP['R']][0]/self.config.distance)
print 'angular radius = %f arcsec' % ((180.0/np.pi)*3600.0*angularDiameter/2.0)
### Plot data
if plot:
self.plotTP()
self.plotGas()
self.plotCloud()
self.plotProp()
return self.nAtm
def run(self, Pmin=None, Pmax=None, regridType=None, gasType=None, cloudType=None, otherType=None, tweak=True):
"""This is the standard pipeline"""
# ##Set run defaults
if Pmin is None:
Pmin = self.config.pmin
if Pmax is None:
Pmax = self.config.pmax
if regridType is None:
regridType = self.config.regridType
if gasType is None:
gasType = self.config.gasType
if cloudType is None:
cloudType = self.config.cloudType
if otherType is None:
otherType = self.config.otherType
# ## Generate gas profile
if gasType not in self.gasGen.keys():
print('Error: No such gasType: ', gasType)
return 0
else:
self.gasGen[gasType]()
self.nAtm = len(self.gas[0])
if self.batch_mode:
return self.nAtm
self.chem = {}
for key in self.config.C:
if key in ['P', 'T', 'Z', 'DZ']:
continue
self.chem[key] = chemistry.ConstituentProperties(key)
# ## Generate cloud profile
if cloudType not in self.cloudGen.keys():
print('Error: No such cloudType: ', cloudType)
return 0
else:
self.cloudGen[cloudType]()
# ## Put onto common grid
if self.verbose:
print("Regrid: {}".format(regridType))
regridded = regrid.regrid(self, regridType=regridType, Pmin=Pmin, Pmax=Pmax)
self.nAtm = len(self.gas[0])
if tweak: # This loads and calls the module as given in the config.par tweakmodule parameter
self.tweakAtm()
# ## Compute other parameters that are needed
if self.config.otherType not in self.propGen.keys():
print('Error: no such otherTpe: ', otherType)
return 0
else:
self.propGen[otherType]()
angularDiameter = 2.0 * math.atan(self.layerProperty[self.config.LP['R']][0] / self.config.distance)
if self.verbose == 'loud':
print('angular radius = {} arcsec'.format(utils.r2asec(angularDiameter / 2.0)))
# ## Plot data
if self.plot:
self.plotTP()
self.plotGas()
self.plotCloud()
self.plotProp()
return self.nAtm
def run(self,
Pmin=None,
Pmax=None,
regridType=None,
gasType=None,
cloudType=None,
otherType=None,
tweak=True):
"""This is the standard pipeline"""
# ##Set run defaults
if Pmin is None:
Pmin = self.config.pmin
if Pmax is None:
Pmax = self.config.pmax
if regridType is None:
regridType = self.config.regridType
if gasType is None:
gasType = self.config.gasType
if cloudType is None:
cloudType = self.config.cloudType
if otherType is None:
otherType = self.config.otherType
# ## Generate gas profile
if gasType not in self.gasGen.keys():
print('Error: No such gasType: ', gasType)
return 0
else:
self.gasGen[gasType]()
self.nAtm = len(self.gas[0])
if self.batch_mode:
return self.nAtm
self.chem = {}
for key in self.config.C:
if key in ['P', 'T', 'Z', 'DZ']:
continue
self.chem[key] = chemistry.ConstituentProperties(key)
# ## Generate cloud profile
if cloudType not in self.cloudGen.keys():
print('Error: No such cloudType: ', cloudType)
return 0
else:
self.cloudGen[cloudType]()
# ## Put onto common grid
if self.verbose:
print("Regrid: {}".format(regridType))
regridded = regrid.regrid(self,
regridType=regridType,
Pmin=Pmin,
Pmax=Pmax)
self.nAtm = len(self.gas[0])
if tweak: # This loads and calls the module as given in the config.par tweakmodule parameter
self.tweakAtm()
# ## Compute other parameters that are needed
if self.config.otherType not in self.propGen.keys():
print('Error: no such otherTpe: ', otherType)
return 0
else:
self.propGen[otherType]()
angularDiameter = 2.0 * math.atan(
self.layerProperty[self.config.LP['R']][0] / self.config.distance)
if self.verbose == 'loud':
print('angular radius = {} arcsec'.format(
utils.r2asec(angularDiameter / 2.0)))
# ## Plot data
if self.plot:
self.plotTP()
self.plotGas()
self.plotCloud()
self.plotProp()
return self.nAtm
