def mytest_remap(self, n2p) :
# Make sure we have the right file
self.assertEqual(self.exgrid_name, self.grid1_name + '-' + self.grid2_name)
# ---------- Read some data
val1 = self.val1h[0,:]
# =========== Mask out self.overlap matrix (maske=True means ignore)
# (to get correct conservation numbers)
mask1 = (np.isnan(val1)).astype(np.int32)
mask2 = None
overlap_masked = glint2.mask_out(self.overlap, mask1, mask2)
area1_masked = giss.util.sum_by_rows(overlap_masked)
area2_masked = giss.util.sum_by_cols(overlap_masked)
# ========== Now try it out
# Matrix to go from PROJECTED grid1 to grid2
mat = glint2.grid1_to_grid2(overlap_masked)
# Diagonal matrix converts from a vector of values in the native
# grid to a vector of values in the projected grid.
if n2p :
diag = glint2.proj_native_area_correct(self.grid1, self.sproj, 'n2p')
glint2.multiply_bydiag(mat, diag)
# Regrid
val2 = glint2.coo_multiply(mat, val1, fill=np.nan)
# Conservation sum with "native" grid1 on a sphere
total1_native = np.nansum(area1_masked * (self.native_area1 / self.proj_area1) * val1.reshape(-1))
# Conservation sum with "projected" grid1 on a plane
total1_proj = np.nansum(area1_masked * val1.reshape(-1))
total2 = np.nansum(area2_masked * val2)
print 'n2p = %d' % n2p
print 'total1_native = %f' % total1_native
print 'total1_proj = %f' % total1_proj
print 'total2 = %f' % total2
# Make sure we got it right
if n2p :
total1 = total1_native
else :
total1 = total1_proj
# self.assertTrue(abs(total1 - total2) < 1e-12 * abs(total1))
self.assertSigEqual(total1, total2, epsilon=1e-12)
# ================ Plot It!
# Plot multiple plots on one page
figure = matplotlib.pyplot.figure(figsize=(8.5,11))
# ---------- Plot the GCM Grid
ax = figure.add_subplot(121)
plotter = glint2.Grid_read_plotter(self.grid1_fname, 'grid')
mymap = giss.basemap.greenland_laea(ax)
#mymap = giss.basemap.north_laea(ax)
im = plotter.pcolormesh(mymap, val1)
mymap.colorbar(im, "right", size='5%')
mymap.drawcoastlines()
# ---------- Plot the ice grid
ax = figure.add_subplot(122)
plotter = glint2.Grid_read_plotter(self.grid2_fname, 'grid')
mymap = giss.basemap.greenland_laea(ax)
#mymap = giss.basemap.north_laea(ax)
im = plotter.pcolormesh(mymap, val2)
mymap.colorbar(im, "right", size='5%')
mymap.drawcoastlines()
fname = 'test_remap-%s.png' % ('native' if n2p else 'proj')
figure.savefig(fname)
print '====> Wrote output to %s' % fname
def mytest_remap(self, n2p):
# Make sure we have the right file
self.assertEqual(self.exgrid_name,
self.grid1_name + '-' + self.grid2_name)
# ---------- Read some data
val1 = self.val1h[0, :]
# =========== Mask out self.overlap matrix (maske=True means ignore)
# (to get correct conservation numbers)
mask1 = (np.isnan(val1)).astype(np.int32)
mask2 = None
overlap_masked = glint2.mask_out(self.overlap, mask1, mask2)
area1_masked = giss.util.sum_by_rows(overlap_masked)
area2_masked = giss.util.sum_by_cols(overlap_masked)
# ========== Now try it out
# Matrix to go from PROJECTED grid1 to grid2
mat = glint2.grid1_to_grid2(overlap_masked)
# Diagonal matrix converts from a vector of values in the native
# grid to a vector of values in the projected grid.
if n2p:
diag = glint2.proj_native_area_correct(self.grid1, self.sproj,
'n2p')
glint2.multiply_bydiag(mat, diag)
# Regrid
val2 = glint2.coo_multiply(mat, val1, fill=np.nan)
# Conservation sum with "native" grid1 on a sphere
total1_native = np.nansum(area1_masked *
(self.native_area1 / self.proj_area1) *
val1.reshape(-1))
# Conservation sum with "projected" grid1 on a plane
total1_proj = np.nansum(area1_masked * val1.reshape(-1))
total2 = np.nansum(area2_masked * val2)
print 'n2p = %d' % n2p
print 'total1_native = %f' % total1_native
print 'total1_proj = %f' % total1_proj
print 'total2 = %f' % total2
# Make sure we got it right
if n2p:
total1 = total1_native
else:
total1 = total1_proj
# self.assertTrue(abs(total1 - total2) < 1e-12 * abs(total1))
self.assertSigEqual(total1, total2, epsilon=1e-12)
# ================ Plot It!
# Plot multiple plots on one page
figure = matplotlib.pyplot.figure(figsize=(8.5, 11))
# ---------- Plot the GCM Grid
ax = figure.add_subplot(121)
plotter = glint2.Grid_read_plotter(self.grid1_fname, 'grid')
mymap = giss.basemap.greenland_laea(ax)
#mymap = giss.basemap.north_laea(ax)
im = plotter.pcolormesh(mymap, val1)
mymap.colorbar(im, "right", size='5%')
mymap.drawcoastlines()
# ---------- Plot the ice grid
ax = figure.add_subplot(122)
plotter = glint2.Grid_read_plotter(self.grid2_fname, 'grid')
mymap = giss.basemap.greenland_laea(ax)
#mymap = giss.basemap.north_laea(ax)
im = plotter.pcolormesh(mymap, val2)
mymap.colorbar(im, "right", size='5%')
mymap.drawcoastlines()
fname = 'test_remap-%s.png' % ('native' if n2p else 'proj')
figure.savefig(fname)
print '====> Wrote output to %s' % fname
def mytest_hc(self, n2p) :
# ---------- Set up height classes & corresponding height points
hcmax = [25.]
hcmax.extend(np.array(range(1,40))*100.0)
hcmax = np.array(hcmax)
print hcmax
hpdefs = np.array([
0., 50., 150., 250., 350., 450., 550., 650.,
750., 850., 950., 1050., 1150., 1250., 1350., 1450.,
1550., 1650., 1750., 1850., 1950., 2050., 2150., 2250.,
2350., 2450., 2550., 2650., 2750., 2850., 2950., 3050.,
3150., 3250., 3350., 3450., 3550., 3650., 3750., 3850.])
# ========== Height-classify overlap matrix
self.assertEqual(len(hcmax), self.nhp)
overlaph = glint2.height_classify(self.overlap, self.elev2, hcmax)
# =========== Mask out overlap matrix
# (to get correct conservation numbers)
mask1h = (np.isnan(self.val1h)).astype(np.int32)
#mask2 = mask2
overlaph_m = glint2.mask_out(overlaph, mask1h, self.mask2)
area1h_masked = giss.util.sum_by_rows(overlaph_m)
area2_masked = giss.util.sum_by_cols(overlaph_m)
# Matrix to go from PROJECTED grid1 to grid2
mat_1hc_to_2 = glint2.grid1_to_grid2(overlaph_m)
# ...and back
mat_2_to_1hc = glint2.grid2_to_grid1(overlaph_m)
# Diagonal matrix converts from a vector of values in the native
# grid to a vector of values in the projected grid.
if n2p :
diag = glint2.proj_native_area_correct(self.grid1, self.sproj, 'n2p')
# Convert the native area correction to height-classified space
diag = np.tile(diag, (1,self.nhp)).reshape(-1)
glint2.multiply_bydiag(mat_1hc_to_2, diag)
# -------- Area-weighted remapping using height classes
val2_hc = glint2.coo_multiply(mat_1hc_to_2, self.val1h, fill=np.nan)
# ========== Interpolate to ice grid, using height point smoothing
# Collapse height-classified mask1 into non-height-classified version
mask1 = np.array(mask1h.sum(axis=0)).reshape(mask1h.shape[1:]).astype(np.int32)
# Mask overlap matrix using this (for correct conservation checking)
overlap_m = glint2.mask_out(self.overlap, mask1, self.mask2)
mat_1hp_to_2 = glint2.hp_interp(overlap_m, self.elev2, hpdefs)
val2_hp = glint2.coo_multiply(mat_1hp_to_2, self.val1h, fill=np.nan)
# This is what you get when you start with val1h
# (interpreted as height points), regrid to grid2,
# then back to grid1h (interpreted as height classes)
val1hcx = glint2.coo_multiply(mat_2_to_1hc, val2_hp, fill=np.nan)
# ===============================================
# Evaluate conservation
# A bit more complication extending native_area1 to full hc scheme
factor1h = np.tile(self.native_area1 / self.proj_area1, (self.nhp,1)).reshape(-1)
total1h_native = np.nansum(area1h_masked * factor1h * self.val1h.reshape(-1))
total1h_proj = np.nansum(area1h_masked * self.val1h.reshape(-1))
total2_hc = np.nansum(area2_masked * val2_hc)
total2_hp = np.nansum(area2_masked * val2_hp)
total1hcx = np.nansum(area1h_masked * val1hcx)
print 'total1h_native = %f' % total1h_native
print 'total1h_proj = %f' % total1h_proj
print 'total2_hc = %f' % total2_hc
print 'total2_hp = %f' % total2_hp
print 'total1hcx = %f' % total1hcx
if n2p :
self.assertSigEqual(total1h_native, total2_hc, epsilon=1e-12)
else :
self.assertSigEqual(total1h_proj, total2_hc, epsilon=1e-12)
self.assertNotSigEqual(total1h_native, total1h_proj, epsilon=1e-12)
self.assertSigEqual(total2_hp, total1hcx, epsilon=1e-12)
# Plot multiple plots on one page
figure = matplotlib.pyplot.figure(figsize=(8.5,11))
# ---------- Plot the ice grid (height point interpolation)
ax = figure.add_subplot(121)
plotter = glint2.Grid_read_plotter(self.grid2_fname, 'grid')
mymap = giss.basemap.greenland_laea(ax)
#mymap = giss.basemap.north_laea(ax)
im = plotter.pcolormesh(mymap, val2_hp)
mymap.colorbar(im, "right", size='5%')
mymap.drawcoastlines()
# ---------- Plot with height classes, after going through ice grid
ax = figure.add_subplot(122)
plotter = glint2.Plotter_HC(
glint2.Grid_read_plotter(self.grid2_fname, 'grid'),
overlap_m, self.elev2, hcmax)
mymap = giss.basemap.greenland_laea(ax)
#mymap = giss.basemap.north_laea(ax)
im = plotter.pcolormesh(mymap, val1hcx)
mymap.colorbar(im, "right", size='5%')
mymap.drawcoastlines()
fname = 'test_hc-%s.png' % ('native' if n2p else 'proj')
figure.savefig(fname)
print '====> Wrote output to %s' % fname
def mytest_hc(self, n2p):
# ---------- Set up height classes & corresponding height points
hcmax = [25.]
hcmax.extend(np.array(range(1, 40)) * 100.0)
hcmax = np.array(hcmax)
print hcmax
hpdefs = np.array([
0., 50., 150., 250., 350., 450., 550., 650., 750., 850., 950.,
1050., 1150., 1250., 1350., 1450., 1550., 1650., 1750., 1850.,
1950., 2050., 2150., 2250., 2350., 2450., 2550., 2650., 2750.,
2850., 2950., 3050., 3150., 3250., 3350., 3450., 3550., 3650.,
3750., 3850.
])
# ========== Height-classify overlap matrix
self.assertEqual(len(hcmax), self.nhp)
overlaph = glint2.height_classify(self.overlap, self.elev2, hcmax)
# =========== Mask out overlap matrix
# (to get correct conservation numbers)
mask1h = (np.isnan(self.val1h)).astype(np.int32)
#mask2 = mask2
overlaph_m = glint2.mask_out(overlaph, mask1h, self.mask2)
area1h_masked = giss.util.sum_by_rows(overlaph_m)
area2_masked = giss.util.sum_by_cols(overlaph_m)
# Matrix to go from PROJECTED grid1 to grid2
mat_1hc_to_2 = glint2.grid1_to_grid2(overlaph_m)
# ...and back
mat_2_to_1hc = glint2.grid2_to_grid1(overlaph_m)
# Diagonal matrix converts from a vector of values in the native
# grid to a vector of values in the projected grid.
if n2p:
diag = glint2.proj_native_area_correct(self.grid1, self.sproj,
'n2p')
# Convert the native area correction to height-classified space
diag = np.tile(diag, (1, self.nhp)).reshape(-1)
glint2.multiply_bydiag(mat_1hc_to_2, diag)
# -------- Area-weighted remapping using height classes
val2_hc = glint2.coo_multiply(mat_1hc_to_2, self.val1h, fill=np.nan)
# ========== Interpolate to ice grid, using height point smoothing
# Collapse height-classified mask1 into non-height-classified version
mask1 = np.array(mask1h.sum(axis=0)).reshape(mask1h.shape[1:]).astype(
np.int32)
# Mask overlap matrix using this (for correct conservation checking)
overlap_m = glint2.mask_out(self.overlap, mask1, self.mask2)
mat_1hp_to_2 = glint2.hp_interp(overlap_m, self.elev2, hpdefs)
val2_hp = glint2.coo_multiply(mat_1hp_to_2, self.val1h, fill=np.nan)
# This is what you get when you start with val1h
# (interpreted as height points), regrid to grid2,
# then back to grid1h (interpreted as height classes)
val1hcx = glint2.coo_multiply(mat_2_to_1hc, val2_hp, fill=np.nan)
# ===============================================
# Evaluate conservation
# A bit more complication extending native_area1 to full hc scheme
factor1h = np.tile(self.native_area1 / self.proj_area1,
(self.nhp, 1)).reshape(-1)
total1h_native = np.nansum(area1h_masked * factor1h *
self.val1h.reshape(-1))
total1h_proj = np.nansum(area1h_masked * self.val1h.reshape(-1))
total2_hc = np.nansum(area2_masked * val2_hc)
total2_hp = np.nansum(area2_masked * val2_hp)
total1hcx = np.nansum(area1h_masked * val1hcx)
print 'total1h_native = %f' % total1h_native
print 'total1h_proj = %f' % total1h_proj
print 'total2_hc = %f' % total2_hc
print 'total2_hp = %f' % total2_hp
print 'total1hcx = %f' % total1hcx
if n2p:
self.assertSigEqual(total1h_native, total2_hc, epsilon=1e-12)
else:
self.assertSigEqual(total1h_proj, total2_hc, epsilon=1e-12)
self.assertNotSigEqual(total1h_native, total1h_proj, epsilon=1e-12)
self.assertSigEqual(total2_hp, total1hcx, epsilon=1e-12)
# Plot multiple plots on one page
figure = matplotlib.pyplot.figure(figsize=(8.5, 11))
# ---------- Plot the ice grid (height point interpolation)
ax = figure.add_subplot(121)
plotter = glint2.Grid_read_plotter(self.grid2_fname, 'grid')
mymap = giss.basemap.greenland_laea(ax)
#mymap = giss.basemap.north_laea(ax)
im = plotter.pcolormesh(mymap, val2_hp)
mymap.colorbar(im, "right", size='5%')
mymap.drawcoastlines()
# ---------- Plot with height classes, after going through ice grid
ax = figure.add_subplot(122)
plotter = glint2.Plotter_HC(
glint2.Grid_read_plotter(self.grid2_fname, 'grid'), overlap_m,
self.elev2, hcmax)
mymap = giss.basemap.greenland_laea(ax)
#mymap = giss.basemap.north_laea(ax)
im = plotter.pcolormesh(mymap, val1hcx)
mymap.colorbar(im, "right", size='5%')
mymap.drawcoastlines()
fname = 'test_hc-%s.png' % ('native' if n2p else 'proj')
figure.savefig(fname)
print '====> Wrote output to %s' % fname
