def to_lon_lat_boundary_coordinates(coordinates, params):
for i in range(len(coordinates)):
coordinates[i] = su.convert_index_to_lonlat(coordinates[i][0],
coordinates[i][1],
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
return coordinates
def to_lon_lat_normals(xCoords, yCoords, lineCentrePoints, ontoShelfDirection,
params):
for i in range(len(xCoords)):
xCoord = xCoords[i]
yCoord = yCoords[i]
lon1, lat1 = su.convert_index_to_lonlat(xCoord[0],
yCoord[0],
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
lon2, lat2 = su.convert_index_to_lonlat(xCoord[1],
yCoord[1],
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
xCoords[i] = (lon1, lon2)
yCoords[i] = (lat1, lat2)
#c, d = su.convert_index_to_lonlat(lineCentrePoints[i][0]+ontoShelfDirection[i][0], lineCentrePoints[i][1]+ontoShelfDirection[i][1], params.pixelRes, lon0=lineCentrePoints[i][0], lat0=lineCentrePoints[i][1]);
lineCentrePoints[i] = su.convert_index_to_lonlat(
lineCentrePoints[i][0],
lineCentrePoints[i][1],
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
ontoShelfDirection[i] = ontoShelfDirection[i] / np.linalg.norm(
ontoShelfDirection[i])
ontoShelfDirection[i] = su.convert_index_to_lonlat(
ontoShelfDirection[i][0],
ontoShelfDirection[i][1],
params.pixelRes,
lon0=0,
lat0=0)
#a, b = lineCentrePoints[i];
#ontoShelfDirection[i] = (c-a, d-b);
return xCoords, yCoords, lineCentrePoints, ontoShelfDirection
unp.isnan(skimOntoShelfLong)
== False) | (unp.isnan(truthOntoShelfLong) == False)
xcoords = xcoords[toKeep]
ycoords = ycoords[toKeep]
eksOntoShelfLong = eksOntoShelfLong[toKeep]
altiOntoShelfLong = altiOntoShelfLong[toKeep]
skimOntoShelfLong = skimOntoShelfLong[toKeep]
truthOntoShelfLong = truthOntoShelfLong[toKeep]
#Converts coordinates into lon/lat.
lats = []
lons = []
for i in range(len(xcoords)):
lon, lat = su.convert_index_to_lonlat(xcoords[i],
ycoords[i],
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
lats.append(lat)
lons.append(lon)
###############n#############################
# Plot Jan, Feb, Mar nekman vs ngeostrophic #
##################n##########################
if totalMap:
climMax = 0.4
climMin = -0.15
plt.figure(figsize=figsize)
mapFig = Basemap(llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,
color=(0.3, 0.3, 0.3),
fontsize=ticksize)
#Bathometry
lon = np.arange(params.originLon, params.maxLon, 0.25)
lat = np.arange(params.originLat, params.maxLat, -0.25)
x, y = np.meshgrid(lon, lat)
xi, yi = mapFig(x, y)
cs = mapFig.pcolormesh(xi, yi, depth)
#, cmap=cmap);
#Plot intersected grids
for gridIntercept in gridIntercepts:
lon1, lat1 = su.convert_index_to_lonlat(gridIntercept.bx1,
gridIntercept.by1,
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
lon2, lat2 = su.convert_index_to_lonlat(gridIntercept.bx2,
gridIntercept.by2,
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
draw_box(mapFig, lon1, lat1, lon2, lat2, color='r')
#Plot line segments:
colour = 'y'
for gridIntercept in gridIntercepts:
xs = (gridIntercept.i1lon, gridIntercept.i2lon)
ys = (gridIntercept.i1lat, gridIntercept.i2lat)
x, y = mapFig(xs, ys)
mapFig.plot(x, y, color=colour)
data.ycoords = data.ycoords[toKeep]
####################################
# Process data for Jan, Feb, March #
####################################
if plot123:
data = get_processed_data([0, 1, 2], allData)
if resampleFraction > 1:
resample_data(data, resampleFraction)
#Converts coordinates into lon/lat.
lats = []
lons = []
for i in range(len(data.xcoords)):
lon, lat = su.convert_index_to_lonlat(data.xcoords[i], data.ycoords[i],
params.pixelRes)
lats.append(lat)
lons.append(lon)
###############n#############################
# Plot Jan, Feb, Mar nekman vs ngeostrophic #
##################n##########################
if plotEkmanVsGeostrophic:
plt.figure(figsize=figsize)
mapFig = Basemap(llcrnrlon=-180.0,
llcrnrlat=-90,
urcrnrlon=180.0,
urcrnrlat=90.0,
resolution='l',
projection='cyl',
lat_0=39.5,