def colorPixel(a, points):
#mindist = 100
cur = 0
totalDist = 0
for point in points:
distance = dist(a, toPixels(point[0], point[1]))
#mindist = min(distance, mindist)
cur += (point[2] - 0.5) / distance
totalDist += 1 / distance
#print(cur, totalDist, cur/totalDist)
cur = 0.5 + cur / totalDist
return valToColor(cur, coldest, hottest)
def get_w_depth(xi, yi, url='http://geoport.whoi.edu/thredds/dodsC/bathy/gom03_v03'):
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] - 0.000834 and i < xi[0] + 0.000834:
xgom.append(i)
for i in ygom_array:
if i > yi[0] - 0.000834 and i < yi[0] + 0.000834:
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 = dist(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
f.seek(0, 2)
write_line(f, data)
#def save_excel1(filename, data, head):
# dexcel = xlwt.Workbook()
# dtable = dexcel.sheets()[0]
# rows = dtable.nrows
# if not rows:
# for i in head:
# dtable.put_cell(rows, head.index(i), i)
# dtable.put_cell(rows+1, head.index(i), data[head.index(i)])
# else:
# for i in data:
# dtable.put_cell(rows, data.index(i), data[data.index(i)])
# dexcel.save
time_input = input_with_default('the time you want to calculate', '11-21 11:33')
time_cal = time.strptime(time_input, "%m-%d %H:%M")
ID = input_with_default('drifter ID', 130410701)
lat_fcasted = input_with_default('latitude forecasted', 4150.1086)
lont_fcasted = input_with_default('longitude forecasted', 7005.7876)
datafile = r'http://www.nefsc.noaa.gov/drifter/drift_audubon_2013_1.dat'
f = urlopen(datafile)
#f = open(datafile)
coor = pos_real(f, time_cal, ID)
distance = dist(coor[0], coor[1], float(lat_fcasted), float(lont_fcasted))
print distance[0]
data = (time_input, ID, str(distance[0]))
#save_excel('distance.xls', data = (time_input, ID, str(distance[0])))
fs = open('distance.dat', 'a+')
write_data(fs, data, 'date ID distance')
fs.close()
def get_w_depth(xi,
yi,
url='http://geoport.whoi.edu/thredds/dodsC/bathy/gom03_v03'):
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] - 0.000834 and i < xi[0] + 0.000834:
xgom.append(i)
for i in ygom_array:
if i > yi[0] - 0.000834 and i < yi[0] + 0.000834:
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 = dist(
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
#def save_excel1(filename, data, head):
# dexcel = xlwt.Workbook()
# dtable = dexcel.sheets()[0]
# rows = dtable.nrows
# if not rows:
# for i in head:
# dtable.put_cell(rows, head.index(i), i)
# dtable.put_cell(rows+1, head.index(i), data[head.index(i)])
# else:
# for i in data:
# dtable.put_cell(rows, data.index(i), data[data.index(i)])
# dexcel.save
time_input = input_with_default('the time you want to calculate',
'11-21 11:33')
time_cal = time.strptime(time_input, "%m-%d %H:%M")
ID = input_with_default('drifter ID', 130410701)
lat_fcasted = input_with_default('latitude forecasted', 4150.1086)
lont_fcasted = input_with_default('longitude forecasted', 7005.7876)
datafile = r'http://www.nefsc.noaa.gov/drifter/drift_audubon_2013_1.dat'
f = urlopen(datafile)
#f = open(datafile)
coor = pos_real(f, time_cal, ID)
distance = dist(coor[0], coor[1], float(lat_fcasted), float(lont_fcasted))
print distance[0]
data = (time_input, ID, str(distance[0]))
#save_excel('distance.xls', data = (time_input, ID, str(distance[0])))
fs = open('distance.dat', 'a+')
write_data(fs, data, 'date ID distance')
fs.close()
