else:
days=jd_id[-1]+366-jd_id[0]
elif num_times_strattle_new_year==2:
days=jd_id[-1]+365+(365-jd_id[0])
elif num_times_strattle_new_year==3:# added this case when Lester Bell strattled the new year 3 times!!!
days=jd_id[-1]+365+365+(365-jd_id[0])
totaldays+=days
# get name of school and teacher
[PI,lab,depth]=read_codes_names(id_list[i]) # gets name, lab/school, and depth from codes.dat
#get the totdist
distkm,bear=[],[]
for n in range(1,len(lat_id)):
#d,b = dist(lat_id[n-1], lon_id[n-1], lat_id[n], lon_id[n])
d,b = distance((lat_id[n-1], lon_id[n-1]), (lat_id[n], lon_id[n]))
distkm.append(d)
bear.append(b)
totdist=sum(distkm)
totaldistance+=totdist
#get the s2end
#s2end,bear2=dist(lat_id[0],lon_id[0],lat_id[-1],lon_id[-1])
s2end,bear2=distance((lat_id[0],lon_id[0]),(lat_id[-1],lon_id[-1]))
#get the u,v,spd
u,v,spd,jdn = ll2uv(jd_id,lat_id,lon_id)
# maybe we should be using a different time here
#get the meanu,meanv,meanspd
meanu = numpy.mean(u)
meanv = numpy.mean(v)
meanspd = numpy.mean(spd)
def multi_track(drifter_ids, depth, days, lat_incr, lon_incr, starttime):
'''
This function retrieves all the data needed and returns it all
'''
drifter = get_drifter(ID) # Retrive drifter data
print ID
if starttime:
if days:
nodes_drifter = drifter.get_track(starttime,days)
else:
nodes_drifter = drifter.get_track(starttime)
else:
nodes_drifter = drifter.get_track()
''' determine latitude, longitude, start, and end times of the drifter?'''
lon, lat = nodes_drifter['lon'][0], nodes_drifter['lat'][0]
# adjust for the added 5 hours in the models
starttime = nodes_drifter['time'][0]-timedelta(hours=5)
endtime = nodes_drifter['time'][-1]-timedelta(hours=5)
print starttime
''' read data points from fvcom and roms websites and store them'''
mod = '30yr' # mod has to be '30yr' or 'GOM3' or 'massbay'
get_fvcom_obj = get_fvcom(mod)
url_fvcom = get_fvcom_obj.get_url(starttime, endtime)
nodes_fvcom = get_fvcom_obj.get_track(lon,lat,depth,url_fvcom) # iterates fvcom's data
get_roms_obj = get_roms()
url_roms = get_roms_obj.get_url(starttime, endtime)
nodes_roms = get_roms_obj.get_track(lon, lat, depth, url_roms)
if type(nodes_roms['lat']) == np.float64: # ensures that the single point case still functions properly
nodes_roms['lon'] = [nodes_roms['lon']]
nodes_roms['lat'] = [nodes_roms['lat']]
'''Calculate the distance seperation'''
dist_roms = distance((nodes_drifter['lat'][-1],nodes_drifter['lon'][-1]),(nodes_roms['lat'][-1],nodes_roms['lon'][-1]))
dist_fvcom = distance((nodes_drifter['lat'][-1],nodes_drifter['lon'][-1]),(nodes_fvcom['lat'][-1],nodes_fvcom['lon'][-1]))
print 'The seperation of roms was %f and of fvcom was %f kilometers from drifter %s' % (dist_roms[0], dist_fvcom[0], ID )
''' set latitude and longitude arrays for basemap'''
lonsize = [min_data(nodes_drifter['lon'],nodes_fvcom['lon']),
max_data(nodes_drifter['lon'],nodes_fvcom['lon'])]
latsize = [min_data(nodes_drifter['lat'],nodes_fvcom['lat']),
max_data(nodes_drifter['lat'],nodes_fvcom['lat'])]
diff_lon = .1
diff_lat = .1
lonsize = [lonsize[0]-diff_lon,lonsize[1]+diff_lon]
latsize = [latsize[0]-diff_lat,latsize[1]+diff_lat]
return nodes_drifter, nodes_roms, nodes_fvcom, lonsize, latsize, starttime
def get_w_depth(xi,yi):
url='http://geoport.whoi.edu/thredds/dodsC/bathy/gom03_v1_0'#url='http://geoport.whoi.edu/thredds/dodsC/bathy/crm_vol1.nc' ):
if xi[0]>999.: # if it comes in decimal -minutes, conert it
#for kk in range(len(xi)):
(y2,x2)=dm2dd(yi[0],xi[0])
yi[0]=y2
xi[0]=x2
try:
dataset = open_url(url)
except:
print 'Sorry, ' + url + ' is not available'
sys.exit(0)
#read lat, lon,topo from url
xgom_array = dataset['lon']
ygom_array = dataset['lat']
dgom_array = dataset['topo'].topo
#print dgom_array.shape, xgom_array[5:9],dgom_array[5]
#convert the array to a list
xgom, ygom = [], []
for i in xgom_array:
if i > xi[0] - 0.00834 and i < xi[0] + 0.00834:
xgom.append(i)
for i in ygom_array:
if i > yi[0] - 0.00834 and i < yi[0] + 0.00834:
ygom.append(i)
x_index, y_index = [], []
(ys, xs) = dgom_array.shape
for i in range(0, len(xgom)):
x_index.append(int(round(np.interp(xgom[i], xgom_array, range(xs)))))
for i in range(0, len(ygom)):
y_index.append(int(round(np.interp(ygom[i], ygom_array, range(ys)))))
dep, distkm, dist1 = [], [], []
for k in range(len(x_index)):
for j in range(len(y_index)):
dep.append(dgom_array[(y_index[j], x_index[k])])
distkm, b = distance((ygom[j], xgom[k]), (yi[0], xi[0]))
dist1.append(distkm)
#get the nearest,second nearest,third nearest point.
dist_f_nearest = sorted(dist1)[0]
dist_s_nearest = sorted(dist1)[1]
dist_t_nearest = sorted(dist1)[2]
index_dist_nearest = range(len(dist1))
index_dist_nearest.sort(lambda x, y:cmp(dist1[x], dist1[y]))
dep_f_nearest = dep[index_dist_nearest[0]]
dep_s_nearest = dep[index_dist_nearest[1]]
dep_t_nearest = dep[index_dist_nearest[2]]
#compute the finally depth
d1 = dist_f_nearest
d2 = dist_s_nearest
d3 = dist_t_nearest
def1 = dep_f_nearest
def2 = dep_s_nearest
def3 = dep_t_nearest
depth_finally = def1 * d2 * d3 / (d1 * d2 + d2 * d3 + d1 * d3) + def2 * d1 * d3 / (d1 * d2 + d2 * d3 + d1 * d3) + def3 * d2 * d1 / (d1 * d2 + d2 * d3 + d1 * d3)
return depth_finally
get_fvcom_obj = get_fvcom(mod)
url_fvcom = get_fvcom_obj.get_url(starttime, endtime)
nodes_fvcom = get_fvcom_obj.get_track(lon,lat,depth,url_fvcom) # iterates fvcom's data
#get_roms_obj = get_roms()
#url_roms = get_roms_obj.get_url(starttime, endtime)
#nodes_roms = get_roms_obj.get_track(lon, lat, depth, url_roms)
#if type(nodes_roms['lat']) == np.float64: # ensures that the single point case still functions properly
# nodes_roms['lon'] = [nodes_roms['lon']]
# nodes_roms['lat'] = [nodes_roms['lat']]
'''Calculate the distance seperation'''
#dist_roms = distance((nodes_drifter['lat'][-1],nodes_drifter['lon'][-1]),(nodes_roms['lat'][-1],nodes_roms['lon'][-1]))
dist_fvcom = distance((nodes_drifter['lat'][-1],nodes_drifter['lon'][-1]),(nodes_fvcom['lat'][-1],nodes_fvcom['lon'][-1]))
#print 'The seperation of roms was %f and of fvcom was %f kilometers for drifter %s' % (dist_roms[0], dist_fvcom[0], ID )
''' set latitude and longitude arrays for basemap'''
lonsize = [min_data(nodes_drifter['lon'],nodes_fvcom['lon']),
max_data(nodes_drifter['lon'],nodes_fvcom['lon'])]
latsize = [min_data(nodes_drifter['lat'],nodes_fvcom['lat']),
max_data(nodes_drifter['lat'],nodes_fvcom['lat'])]
diff_lon = (lonsize[0]-lonsize[1])*4
diff_lat = (latsize[1]-latsize[0])*4
lonsize = [lonsize[0]-diff_lon,lonsize[1]+diff_lon]
latsize = [latsize[0]-diff_lat,latsize[1]+diff_lat]
def get_w_depth(xi, yi): #xi:lon yi:lat
url = 'http://geoport.whoi.edu/thredds/dodsC/bathy/gom03_v1_0' #url='http://geoport.whoi.edu/thredds/dodsC/bathy/crm_vol1.nc' ):
if xi[0] > 999.: # if it comes in decimal -minutes, conert it
#for kk in range(len(xi)):
(y2, x2) = dm2dd(yi[0], xi[0])
yi[0] = y2
xi[0] = x2
try:
dataset = open_url(url)
except:
print 'Sorry, ' + url + ' is not available'
sys.exit(0)
#read lat, lon,topo from url
xgom_array = dataset['lon']
ygom_array = dataset['lat']
dgom_array = dataset['topo'].topo
#print dgom_array.shape, xgom_array[5:9],dgom_array[5]
#convert the array to a list
xgom, ygom = [], []
for i in xgom_array:
if i > xi[0] - 0.00834 and i < xi[0] + 0.00834:
xgom.append(i)
for i in ygom_array:
if i > yi[0] - 0.00834 and i < yi[0] + 0.00834:
ygom.append(i)
x_index, y_index = [], []
(ys, xs) = dgom_array.shape
for i in range(0, len(xgom)):
x_index.append(int(round(np.interp(xgom[i], xgom_array, range(xs)))))
for i in range(0, len(ygom)):
y_index.append(int(round(np.interp(ygom[i], ygom_array, range(ys)))))
dep, distkm, dist1 = [], [], []
for k in range(len(x_index)):
for j in range(len(y_index)):
dep.append(dgom_array[(y_index[j], x_index[k])])
distkm, b = distance((ygom[j], xgom[k]), (yi[0], xi[0]))
dist1.append(distkm)
#print dist1
if len(dist1) >= 3:
#get the nearest,second nearest,third nearest point.
dist_f_nearest = sorted(dist1)[0]
dist_s_nearest = sorted(dist1)[1]
dist_t_nearest = sorted(dist1)[2]
index_dist_nearest = range(len(dist1))
index_dist_nearest.sort(lambda x, y: cmp(dist1[x], dist1[y]))
dep_f_nearest = dep[index_dist_nearest[0]]
dep_s_nearest = dep[index_dist_nearest[1]]
dep_t_nearest = dep[index_dist_nearest[2]]
#compute the finally depth
d1 = dist_f_nearest
d2 = dist_s_nearest
d3 = dist_t_nearest
def1 = dep_f_nearest
def2 = dep_s_nearest
def3 = dep_t_nearest
depth_finally = def1 * d2 * d3 / (
d1 * d2 + d2 * d3 + d1 * d3) + def2 * d1 * d3 / (
d1 * d2 + d2 * d3 +
d1 * d3) + def3 * d2 * d1 / (d1 * d2 + d2 * d3 + d1 * d3)
else:
depth_finally = np.array([[-9999]])
return depth_finally
def get_w_depth(xi, yi, url='http://geoport.whoi.edu/thredds/dodsC/bathy/gom03_v1_0'):# xi is longitude,like [-69.2,-69.1].yi is latitude,like[41.0,41.1]
try:
dataset = open_url(url)
except:
print 'Sorry, ' + url + ' is not available'
sys.exit(0)
#read lat, lon,topo from url
xgom_array = dataset['lon']
ygom_array = dataset['lat']
dgom_array = dataset['topo'].topo
print np.shape(xgom_array),xgom_array[0],xi[0]
#print dgom_array.shape, xgom_array[5:9],dgom_array[5]
#convert the array to a list
xgom, ygom = [], []
for i in xgom_array:
if i > xi[0] - 0.000834 and i < xi[0] + 0.000834:
#if i > xi[0] - 0.0834 and i < xi[0] + 0.0834:
xgom.append(i)
for i in ygom_array:
if i > yi[0] - 0.000834 and i < yi[0] + 0.000834:
#if i > yi[0] - 0.0834 and i < yi[0] + 0.0834:
ygom.append(i)
print np.shape(xgom)
x_index, y_index = [], []
(ys, xs) = dgom_array.shape
for i in range(0, len(xgom)):
x_index.append(int(round(np.interp(xgom[i], xgom_array, range(xs)))))
for i in range(0, len(ygom)):
y_index.append(int(round(np.interp(ygom[i], ygom_array, range(ys)))))
dep, distkm, dist1 = [], [], []
for k in range(len(x_index)):
for j in range(len(y_index)):
dep.append(dgom_array[(y_index[j], x_index[k])])
#distkm, b = dist(ygom[j], xgom[k], yi[0], xi[0],)
distkm, b = distance((ygom[j], xgom[k]),( yi[0], xi[0]))
dist1.append(distkm)
#get the nearest,second nearest,third nearest point.
dist_f_nearest = sorted(dist1)[0]
dist_s_nearest = sorted(dist1)[1]
dist_t_nearest = sorted(dist1)[2]
index_dist_nearest = range(len(dist1))
index_dist_nearest.sort(lambda x, y:cmp(dist1[x], dist1[y]))
dep_f_nearest = dep[index_dist_nearest[0]]
dep_s_nearest = dep[index_dist_nearest[1]]
dep_t_nearest = dep[index_dist_nearest[2]]
#compute the finally depth
d1 = dist_f_nearest
d2 = dist_s_nearest
d3 = dist_t_nearest
def1 = dep_f_nearest
def2 = dep_s_nearest
def3 = dep_t_nearest
depth_finally = def1 * d2 * d3 / (d1 * d2 + d2 * d3 + d1 * d3) + def2 * d1 * d3 / (d1 * d2 + d2 * d3 + d1 * d3) + def3 * d2 * d1 / (d1 * d2 + d2 * d3 + d1 * d3)
return depth_finally[0]
