def calculateDensities(pfile, limits=[120, 290, -45, 45], p_var=None, output='Density'):
print(pfile)
particles = Dataset(pfile, 'r')
lon = particles.variables['lon']
lat = particles.variables['lat']
#active = particles.variables['active']
time = particles.variables['time']
if p_var is not None:
p_var = particles.variables[p_var]
#lon = lon[range(0,10000), :]
#lat = lat[range(0,10000), :]
#active = particles.variables['active']
#time = time[range(0,10000), :]
class ParticleTrack(object):
lon = []
lat = []
def __init__(self, lon, lat, active=1):
self.lon = lon
self.lat = lat
# for t in range(len(lon)):
# #if active[t] == 1:
# self.pos[t,:,0] = lon[t]
# self.pos[t,0,:] = lat[t]
Particles = []
print('Shape of data')
print(np.shape(lon))
print(np.shape(lat))
print(np.shape(time))
#print('Making particles...')
#for p in range(len(lon[:,0])):
# Particles.append(ParticleTrack(lon[p,:], lat[p,:]))#, active))
lons = np.arange(limits[0], limits[1]+1)
lats = np.arange(limits[2], limits[3]+1)
month_time = np.arange(time[0,1], time[0,-1], 30*24*60*60)
blank = np.zeros([len(month_time), len(lats), len(lons)], dtype=np.float32)
DensityField = Field("TagDensity", blank, lon=lons, lat=lats,
time=month_time)
print(np.shape(DensityField.data))
print('Calculating Density...')
for t in range(len(month_time)):
mid_month = month_time[t] + (30*24*60*60)/2
mid_month_i = np.argmin(abs(time[0,:] - mid_month))
print(mid_month_i)
DensityField.data[t, :, :] = np.transpose(density(DensityField, lon, lat, mid_month_i, particle_val=p_var,
relative=True, area_scale=False))
DensityField.write('%s_density' % output)
def calculateDensityRatio(dfiles, output):
Fields = []
for dfile in dfiles:
print("Loading %s" % dfile)
Fields.append(Field.from_netcdf(dfile,
dimensions={'lon': 'nav_lon', 'lat': 'nav_lat', 'time': 'time_counter', 'data': 'TagDensity'},
filenames=[dfile]))
limits = [0, 0, 0, 0]
limits[0] = np.max([field.lon[0] for field in Fields])
limits[1] = np.min([field.lon[-1] for field in Fields])
limits[2] = np.max([field.lat[0] for field in Fields])
limits[3] = np.min([field.lat[-1] for field in Fields])
#limits[1] = (np.min(field.lon[-1]) for field in Fields)
#limits[2] = (np.max(field.lat[0]) for field in Fields)
#limits[3] = (np.min(field.lat[-1]) for field in Fields)
time_lim = [np.max([field.time[0] for field in Fields]), np.min([field.time[-1] for field in Fields])]
#time_lim = [Fields[0].time[0], Fields[0].time[-1]]
lon = np.arange(start=limits[0], stop=limits[1]+1, dtype=np.float32)
lat = np.arange(start=limits[2], stop=limits[3]+1, dtype=np.float32)
time = np.arange(time_lim[0], time_lim[1]+1, 30*24*60*60, dtype=np.float32)
Ratio = np.zeros([len(time), len(lat), len(lon)], dtype=np.float32)
print(limits)
print(Fields[0].lon)
print(Fields[1].lon)
print(lon)
print(Fields[0].lat)
print(Fields[1].lat)
print(lat)
print(Fields[0].time)
print(Fields[1].time)
print(time)
for t in range(len(time)):
for x in range(len(lon)):
for y in range(len(lat)):
tagged = Fields[0].data[np.where(Fields[0].time == time[t])[0][0],
np.where(Fields[0].lat == lat[y])[0][0],
np.where(Fields[0].lon == lon[x])[0][0]]
pop = Fields[1].data[np.where(Fields[1].time == time[t])[0][0],
np.where(Fields[1].lat == lat[y])[0][0],
np.where(Fields[1].lon == lon[x])[0][0]]
#print('%s - %s' % (tagged, pop))
if pop == 0:
Ratio[t, y, x] = 0
else:
Ratio[t, y, x] = tagged/pop
Ratios = Field('DensityRatio', Ratio, lon, lat, time=time)
Ratios.write(filename=output)
def calculateDensityRatio(dfiles, output):
Fields = []
for dfile in dfiles:
print("Loading %s" % dfile)
Fields.append(
Field.from_netcdf(dfile,
dimensions={
'lon': 'nav_lon',
'lat': 'nav_lat',
'time': 'time_counter',
'data': 'TagDensity'
},
filenames=[dfile]))
limits = [0, 0, 0, 0]
limits[0] = np.max([field.lon[0] for field in Fields])
limits[1] = np.min([field.lon[-1] for field in Fields])
limits[2] = np.max([field.lat[0] for field in Fields])
limits[3] = np.min([field.lat[-1] for field in Fields])
#limits[1] = (np.min(field.lon[-1]) for field in Fields)
#limits[2] = (np.max(field.lat[0]) for field in Fields)
#limits[3] = (np.min(field.lat[-1]) for field in Fields)
time_lim = [
np.max([field.time[0] for field in Fields]),
np.min([field.time[-1] for field in Fields])
]
#time_lim = [Fields[0].time[0], Fields[0].time[-1]]
lon = np.arange(start=limits[0], stop=limits[1] + 1, dtype=np.float32)
lat = np.arange(start=limits[2], stop=limits[3] + 1, dtype=np.float32)
time = np.arange(time_lim[0],
time_lim[1] + 1,
30 * 24 * 60 * 60,
dtype=np.float32)
Ratio = np.zeros([len(time), len(lat), len(lon)], dtype=np.float32)
print(limits)
print(Fields[0].lon)
print(Fields[1].lon)
print(lon)
print(Fields[0].lat)
print(Fields[1].lat)
print(lat)
print(Fields[0].time)
print(Fields[1].time)
print(time)
for t in range(len(time)):
for x in range(len(lon)):
for y in range(len(lat)):
tagged = Fields[0].data[np.where(
Fields[0].time == time[t])[0][0],
np.where(
Fields[0].lat == lat[y])[0][0],
np.where(
Fields[0].lon == lon[x])[0][0]]
pop = Fields[1].data[np.where(Fields[1].time == time[t])[0][0],
np.where(Fields[1].lat == lat[y])[0][0],
np.where(Fields[1].lon == lon[x])[0][0]]
#print('%s - %s' % (tagged, pop))
if pop == 0:
Ratio[t, y, x] = 0
else:
Ratio[t, y, x] = tagged / pop
Ratios = Field('DensityRatio', Ratio, lon, lat, time=time)
Ratios.write(filename=output)
