def get_example_method_plot_params(): #Whole European shelf
params = get_baseline_params();
params.pixelRes = (0.25, 0.25);
params.timeRes = "monthly";
params.originLon = -12.0; #at (0,0)
params.maxLon = 1.0;
params.originLat = 62.5; #at (0,0)
params.maxLat = 51.0;
params.contourPathFile = "method_example_contour_paths.p";
params.contourPathFileDeep = "method_example_contour_paths_deep.p";
params.paramsetName = "method_example";
originLon, originLat = su.convert_lonlat_to_index(params.originLon, params.originLat, params.pixelRes);
maxLon, maxLat = su.convert_lonlat_to_index(params.maxLon, params.maxLat, params.pixelRes);
params.ilonRange = (originLon, maxLon); #(600, 850)
params.ilatRange = (originLat, maxLat); #(50, 225)
params.shallowDepth = 500.0;
params.deepDepth = params.shallowDepth+100.0;
params.contourPathsShallow = [1];
params.contourPathsDeep = [2];
params.numberOfBoundryApproximationLines = [8]; #how many lines should be used to approximate the shelf edge boundry
params.start_year = 2014;
params.end_year = 2015;
params.start_month = 0;
params.end_month = 12;
params.contourMaskFunc = mf.empty_mask_function;
return params;
def get_global_test_params():
params = get_baseline_params();
params.pixelRes = (0.25, 0.25);
params.timeRes = "monthly";
params.originLon = -180.0; #at (0,0)
params.maxLon = 179.75;
params.originLat = 89.75; #at (0,0)
params.maxLat = -90.0;
originLon, originLat = su.convert_lonlat_to_index(params.originLon, params.originLat, params.pixelRes);
maxLon, maxLat = su.convert_lonlat_to_index(params.maxLon, params.maxLat, params.pixelRes);
params.ilonRange = (originLon, maxLon); #(600, 850)
params.ilatRange = (originLat, maxLat); #(50, 225)
params.shallowDepth = 500.0;
params.deepDepth = params.shallowDepth+100.0;
# params.contourPathsShallow = [13, 15, 39, 68, 75, 101, 107, 110, 113, 115, 136, 175, 257, 177, 180];
# params.contourPathsDeep = [2, 3, 21, 41, 45, 67, 66, 74, 77, 79, 97, 129, 168, 131, 133];
# params.numberOfBoundryApproximationLines = [32, 16, 32, 16, 4, 8, 8, 32, 16, 8, 32, 32, 8, 8, 8]; #how many lines should be used to approximate the shelf edge boundry
params.contourMaskFunc = mf.global_shelf_mask_func;
params.start_year = 1993;
params.end_year = 1993;
params.start_month = 0;
params.end_month = 12;
params.contourPathFile = "global_test_shelf_contour_paths.p";
params.contourPathFileDeep = "global_test_shelf_contour_paths_deep.p";
params.paramsetName = "global_test";
return params
def generic_area_mask(allowedAreas, data, params):
keep = np.empty(len(data), dtype=bool)
for pointIndex in range(0, len(data)):
px = data[pointIndex].indexX
py = data[pointIndex].indexY
#Check each area to see if the point falls into one of them
passed = False
for area in allowedAreas:
#convert lon/lat to coordinates
llx, lly = su.convert_lonlat_to_index(area[0][0],
area[0][1],
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
urx, ury = su.convert_lonlat_to_index(area[1][0],
area[1][1],
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
if point_in_area(llx, lly, urx, ury, px, py) == True:
passed = True
break
keep[pointIndex] = passed
#Only keep points that fell into one of the above areas
keep = np.array(keep)
data = list(np.array(data)[keep])
return data
def get_European_shelf_params(): #Whole European shelf
params = get_baseline_params();
params.pixelRes = (0.25, 0.25);
params.timeRes = "monthly";
params.originLon = -25.0; #at (0,0)
params.maxLon = 0.0;
params.originLat = 62.0; #at (0,0)
params.maxLat = 40.0;
params.contourPathFile = "european_shelf_contour_paths.p";
params.contourPathFileDeep = "european_shelf_contour_paths_deep.p";
params.paramsetName = "europeanshelf";
originLon, originLat = su.convert_lonlat_to_index(params.originLon, params.originLat, params.pixelRes);
maxLon, maxLat = su.convert_lonlat_to_index(params.maxLon, params.maxLat, params.pixelRes);
params.ilonRange = (originLon, maxLon); #(600, 850)
params.ilatRange = (originLat, maxLat); #(50, 225)
params.shallowDepth = 500.0;
params.deepDepth = params.shallowDepth+100.0;
params.contourPathsShallow = [1];
params.contourPathsDeep = [1];
#params.numberOfBoundryApproximationLines = [64]; #how many lines should be used to approximate the shelf edge boundry
params.numLineApproximationFunction = lambda x, y : 64; #Function to determine the number of line segments to use
#params.start_year = 2014;
#params.end_year = 2015;
#params.start_month = 0;
#params.end_month = 12;
params.start_year = 2011;
params.end_year = 2012;
params.start_month = 9;
params.end_month = 9;
params.contourMaskFunc = mf.empty_mask_function;
params.yeariMonths = [(params.start_year, imonth) for imonth in range(params.start_month, 12)] + [(params.end_year, imonth) for imonth in range(0, params.end_month)];
return params;
def calculate_km_subsection_bounds_along_shelf(wholeShelfX,
wholeShelfY,
wholeShelfDistances,
subsectionBounds,
params,
testPlot=False):
#Calculate subset mask
subsetMask = [False] * len(wholeShelfX)
for pointIndex in range(0, len(wholeShelfX)):
px = wholeShelfX[pointIndex]
py = wholeShelfY[pointIndex]
#convert lon/lat to coordinates
llx, lly = su.convert_lonlat_to_index(subsectionBounds[0][0],
subsectionBounds[0][1],
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
urx, ury = su.convert_lonlat_to_index(subsectionBounds[1][0],
subsectionBounds[1][1],
params.pixelRes,
lon0=params.originLon,
lat0=params.originLat)
#Check each area to see if the point falls into one of them and set the mask
if point_in_area(llx, lly, urx, ury, px, py) == True:
subsetMask[pointIndex] = True
#find the first and last instance of 'True' for each contiguous span of 'True'
ifirsts = []
ilasts = []
prev = False
for i in range(0, len(subsetMask)):
if subsetMask[i] == True and prev == False:
ifirsts.append(i)
if subsetMask[i] == False and prev == True:
ilasts.append(i - 1)
prev = subsetMask[i]
if prev == True: #last one won't trigger an end of a contiguous block
ilasts.append(i)
if len(ilasts) != len(ifirsts): #sanity check
raise RuntimeError(
"Unequal starts and ends of contiguous blocks in subset filter.")
#convert mask to array
subsetMask = np.array(subsetMask)
cumulativeDists = np.cumsum(wholeShelfDistances)
#calculate intercept distances for each contiguous span
distances = []
for i in range(0, len(ifirsts)):
dist1 = cumulativeDists[ifirsts[i]]
dist2 = cumulativeDists[ilasts[i]]
distances.append((dist1, dist2))
#plot to perform manual sanity check
if testPlot:
import matplotlib.pyplot as plt
from netCDF4 import Dataset
depth = np.flipud(
Dataset("data/GEBCO_bathymetry_0.25x0.25deg.nc",
'r').variables["mean_depth"][:])
plt.figure()
plt.imshow(depth)
for i in range(len(distances)):
plt.plot(wholeShelfX[ifirsts[i]:ilasts[i] + 1],
wholeShelfY[ifirsts[i]:ilasts[i] + 1],
'y',
linewidth=5,
alpha=1.0)
#plot just subsection with thicker line
#plt.plot(wholeShelfX[iFirst:iLast+1], wholeShelfY[iFirst:iLast+1], 'y', linewidth=5, alpha=1.0); #plot just subsection with thicker line
plt.plot(wholeShelfX, wholeShelfY, 'r')
plt.xlim(wholeShelfX.min(), wholeShelfX.max())
plt.ylim(wholeShelfY.max(), wholeShelfY.min())
#return the intercept distances along the shelf boundary
return distances, subsetMask
def global_shelf_mask_func(data, params):
allowedAreas = []
#((llLon, llLat), (urLon, urLat))
allowedAreas.append(area_EuropeanShelf)
#European shelf
allowedAreas.append(area_LabradorSea)
#Labrador Sea
allowedAreas.append(area_MidAtlanticBight)
#Mid-Atlantic Bight
allowedAreas.append(area_CoastOfJapan)
#Coast of Japan
allowedAreas.append(area_Patagonia)
#Patagonia
allowedAreas.append(area_Tasmania)
#Tasmania
allowedAreas.append(area_BarentsSea)
#Barents Sea
allowedAreas.append(area_SouthAtlanticBight)
#South Atlantic Bight
allowedAreas.append(area_SouthGreenland)
#South Greenland
allowedAreas.append(area_AntarcticPeninsula)
#Antarctic Peninsula
allowedAreas.append(area_BeringSeaWest)
#Bering Sea (West)
allowedAreas.append(area_BeringSeaEast)
#Bering Sea (East)
allowedAreas.append(area_IrmingerSea)
#Irminger Sea
allowedAreas.append(area_CascianShelf)
#Cascian Shelf
# allowedAreas.append( ((141.0, -45.0), (151.0, -39.0)) ); #Tasmania
# allowedAreas.append( ((0.0, 60.0), (75.0, 90.0)) ); #Barents Sea
# allowedAreas.append( ((-82.0, 26.0), (-74.0, 36.5)) ); #South Atlantic Bight
# allowedAreas.append( ((-57.0, 59.0), (-18.5, 67.5)) ); #South Greenland
# allowedAreas.append( ((-80.0, -75.0), (-50.0, -61.0)) ); #Antarctic Peninsula
# allowedAreas.append( ((-180.0, 45.0), (-150.0, 61.0)) ); #Bering Sea (West)
# allowedAreas.append( ((178.0, 45.0), (180.0, 61.0)) ); #Bering Sea (East)
# allowedAreas.append( ((-57.0, 59.0), (-18.5, 67.5)) ); #Irminger Sea
# allowedAreas.append( ((-140.0, 40.0), (-120.0, 54.0)) ); #Cascian Shelf
keep = np.empty(len(data), dtype=bool)
for pointIndex in range(0, len(data)):
px = data[pointIndex].indexX
py = data[pointIndex].indexY
#Check each area to see if the point falls into one of them
passed = False
for area in allowedAreas:
#convert lon/lat to coordinates
llx, lly = su.convert_lonlat_to_index(area[0][0], area[0][1],
params.pixelRes)
urx, ury = su.convert_lonlat_to_index(area[1][0], area[1][1],
params.pixelRes)
if point_in_area(llx, lly, urx, ury, px, py) == True:
passed = True
break
keep[pointIndex] = passed
#Only keep points that fell into one of the above areas
keep = np.array(keep)
data = list(np.array(data)[keep])
return data