def show_uboxes(uboxes,S=None,col='b',ecol='k', fig=None):
if uboxes.dim != 2:
raise Exception("show_uboxes: dimension must be 2")
if S is None:
S = range(uboxes.size)
patches = []
for i in S:
art = mpatches.Rectangle(uboxes.corners[i],uboxes.width[0],uboxes.width[1])
patches.append(art)
if not fig:
fig = plt.figure()
ax = fig.gca()
ax.hold(True)
collection = PatchCollection(patches)
collection.set_facecolor(col)
collection.set_edgecolor(ecol)
ax.add_collection(collection,autolim=True)
ax.autoscale_view()
plt.draw()
plt.show()
return fig
def draw(self, ax, colour='k', alpha=1., linestyle='-', linewidth=1., markers=None, facecolor='none', dashes=(1, 1), zorder=0): """ Draw the polygon on ax """ # Add the last point ptstoplot = self.verts.tolist() ptstoplot.append(ptstoplot[0]) ptstoplot = np.array(ptstoplot) if facecolor == 'none': try: if linestyle == '--': ax.plot(ptstoplot[:, 0], ptstoplot[:, 1], c=colour, alpha=alpha, ls=linestyle, dashes=dashes, lw=linewidth, marker=markers, zorder=zorder) else: ax.plot(ptstoplot[:, 0], ptstoplot[:, 1], c=colour, alpha=alpha, ls=linestyle, lw=linewidth, marker=markers, zorder=zorder) except IndexError: pass else: patches = [] filled_pol = mp.Polygon(self.verts) patches.append(filled_pol) collection = PatchCollection(patches, facecolors=facecolor) ax.add_collection(collection) collection.set_alpha(alpha) collection.set_edgecolor(colour) collection.set_linewidth(linewidth) collection.set_linestyle(linestyle)
def color_split_bank_by_svd(filename_template, split_bank_nums, cm_min_vec, cm_max_vec, chi_min_vec, chi_max_vec):
split_bank_nums = numpy.array(split_bank_nums)
cm_min_vec = numpy.array(cm_min_vec)
cm_max_vec = numpy.array(cm_max_vec)
chi_min_vec = numpy.array(chi_min_vec)
chi_max_vec = numpy.array(chi_max_vec)
total_svd_banks = max(split_bank_nums)
# Create rectangles for split-bank tiling plot
patches = []
cm_range_vec = cm_max_vec-cm_min_vec
chi_range_vec = chi_max_vec-chi_min_vec
for cm_range, chi_range, cm_min, chi_min in zip(cm_range_vec, chi_range_vec, cm_min_vec, chi_min_vec):
patches.append(Rectangle((cm_min, chi_min), cm_range, chi_range))
patches = numpy.array(patches)
fig, axes = create_plot(r"$\mathcal{M} (M_{\odot})$", r"$\chi$", aspect = None)
for n in range(total_svd_banks + 1):
p = PatchCollection(patches[split_bank_nums == n], match_original=False, facecolor = choose_color(n), alpha=0.6)
p.set_edgecolor('face')
axes.add_collection(p)
axes.set_xscale('log')
axes.set_xlim(min(cm_min_vec), max(cm_max_vec))
axes.set_ylim(-1, 1)
filename = filename_template % ("split_bank_color_by_svd",)
fig.savefig(filename)
def show_uboxes_corners( corners, width, S=None, col='b', ecol='k' ):
"""
This version takes the corners and width arrays as arguments
instead.
"""
if corners.ndim != 2:
raise Exception("show_uboxes_corners: dimension must be 2")
if S is None:
S = range( len(corners) )
patches = []
for i in S:
art = mpatches.Rectangle( corners[i], width[0], width[1] )
patches.append(art)
fig = plt.figure()
ax = fig.gca()
ax.hold(True)
collection = PatchCollection(patches)
collection.set_facecolor(col)
collection.set_edgecolor(ecol)
ax.add_collection(collection,autolim=True)
ax.autoscale_view()
plt.show()
return fig
def makePlot(F, V, field, varName, outCount, simTime, i_save):
polygons = []
colors = []
maxVal = np.max(field)
for fid in F.ids:
#fid = 25
pos = np.full((3, 2), np.nan)
for i in range(0, 3):
pos[i, 0] = V.lons[F.Vids[fid, i]]
pos[i, 1] = V.lats[F.Vids[fid, i]]
lons = pos[:, 0]
#print(np.std(lons)/F.nf)
if F.nSubdiv == 0:
thresh = 5
elif F.nSubdiv == 1:
thresh = 1
elif F.nSubdiv == 2:
thresh = 0.1
elif F.nSubdiv == 3:
thresh = 0.05
elif F.nSubdiv == 4:
thresh = 0.006
if np.std(lons) / F.nf > thresh:
deltas = F.lons[fid] - lons
condition = np.argwhere((np.abs(deltas) > np.std(lons))
& (lons != 180))
lons[condition] = lons[condition] + (np.sign(deltas) *
360)[condition]
polygon = Polygon(pos, closed=True, linewidth=0)
polygons.append(polygon)
#colors.append(field[fid]/maxVal)
colors.append(field[fid])
#pc = PatchCollection(polygons, alpha=0.8, cmap='copper')
pc = PatchCollection(polygons, alpha=0.8)
pc.set_edgecolor('k')
pc.set_array(np.array(colors))
#fig,ax = plt.subplots(figsize=(20,12))
fig, ax = plt.subplots(figsize=(10, 6))
ax.set_xlim(-90, 540)
ax.set_ylim(-90, 90)
ax.add_collection(pc)
fig.colorbar(pc, ax=ax)
count = str(outCount).zfill(4)
plt.title(count + ' ' + varName + ' day: ' +
str(np.round(simTime / 3600 / 24, 2)))
if i_save:
name = 'out/' + varName + '_' + count + '.png'
plt.savefig(name)
plt.close('all')
else:
plt.show()
#plt.subplots_adjust(0,0,1,1)
return (plt, ax)
def add_blobs(rows, columns, sizes, color=None, ax=None):
"""The function draws circles around given pixel coordinates
Parameters
-----------
rows: array
pixel positions, y axis
columns: array
pixel positions, x axis
sizes:array
Returns
-------
star_circles:
"""
ax = ax or plt.gca()
star_circles = PatchCollection([
Circle((col, row), radius=3 * sigma)
for row, col, sigma in zip(rows, columns, sizes)
])
star_circles.set_facecolor('none')
color = color or next(ax._get_lines.prop_cycler).get('color')
star_circles.set_edgecolor(color)
ax.add_collection(star_circles)
return star_circles
def DrawPolygons(polygons):
#https://stackoverflow.com/questions/32141476/how-to-fill-polygons-with-colors-based-on-a-variable-in-matplotlib
font = {'family': 'serif',
'color': 'black',
'weight': 'normal',
'size': 14,
}
fig,ax = plt.subplots(figsize=(6, 6), dpi=80, facecolor='w', edgecolor='k')
patches=[]
for p in polygons:
patches.append(Patches.Polygon(np.array(p),True))
p = PatchCollection(patches,cmap=matplotlib.cm.rainbow, alpha=0.8)
p.set_edgecolor('k')
colors = 10*np.random.random(len(patches))
p.set_array(np.array(colors))
ax.add_collection(p)
fig.colorbar(p, ax=ax)
plt.axis('equal')
plt.gca().invert_xaxis()
plt.title('Domain Decomposition Map (%d domains)' %(len(patches)), fontdict=font)
plt.xlabel('X', fontdict=font)
plt.ylabel('Y', fontdict=font)
plt.show()
def custom_plot(image, box=None, polygons=None):
#target.extra_fields['masks'].polygons è una lista in cui ogni elemento ha un .polygons
# che è una lista di tensor
fig, ax = plt.subplots(1)
ax.imshow(image)
if box is not None:
for bb in box:
rect = Rectangle((int(bb[0]), int(bb[1])),
int(bb[2]),
int(bb[3]),
linewidth=1,
edgecolor='r',
facecolor='none')
ax.add_patch(rect)
if polygons is not None:
patches = []
for p in polygons:
for p2 in p.polygons:
polygon = Polygon(p2.numpy().reshape((-1, 2)), False)
patches.append(polygon)
p = PatchCollection(patches, alpha=0.4)
p.set_linewidth(2.0)
p.set_edgecolor('r')
ax.add_collection(p)
plt.show()
def init_points(ax, points):
pt_coll = PatchCollection(points, alpha=0.6)
colors = ['limegreen'] * len(points)
pt_coll.set_facecolors(colors)
pt_coll.set_edgecolor('black')
ax.add_collection(pt_coll)
return pt_coll, colors
def visualize2D(ax,x,y,u,colorbarPosition='vertical',colorlimit=None):
xdg0=np.vstack([x.flatten(order='C'),y.flatten(order='C')])
udg0=u.flatten(order='C')
idx=np.asarray([0,1,3,2])
nelem=(x.shape[0]-1)*(x.shape[1]-1)
nelemx=x.shape[1]-1; nelemy=x.shape[0]-1; nelem=nelemx*nelemy
nnx=x.shape[1];nny=x.shape[0]
e2vcg0=gen_e2vcg(x)
udg_ref=udg0[e2vcg0]
cmap=matplotlib.cm.coolwarm
polygon_list=[]
for i in range(nelem):
polygon_=Polygon(xdg0[:,e2vcg0[idx,i]].T)
polygon_list.append(polygon_)
polygon_ensemble=PatchCollection(polygon_list,cmap=cmap,alpha=1)
polygon_ensemble.set_edgecolor('face')
polygon_ensemble.set_array(np.mean(udg_ref,axis=0))
if colorlimit is None:
pass
else:
polygon_ensemble.set_clim(colorlimit)
ax.add_collection(polygon_ensemble)
ax.set_xlim(np.min(xdg0[0,:]),np.max(xdg0[0,:]))
#ax.set_xticks([np.min(xdg0[0,:]),np.max(xdg0[0,:])])
ax.set_ylim(np.min(xdg0[1,:]),np.max(xdg0[1,:]))
#ax.set_yticks([np.min(xdg0[1,:]),np.max(xdg0[1,:])])
#ax.set_aspect('equal')
cbar=plt.colorbar(polygon_ensemble,orientation=colorbarPosition)
return ax,cbar
def make_plot(self,
val,
cct,
clm,
cmap='jet',
cmap_norm=None,
cmap_vmin=None,
cmap_vmax=None):
# make color mapping
smap = ScalarMappable(cmap_norm, cmap)
smap.set_clim(cmap_vmin, cmap_vmax)
smap.set_array(val)
bin_colors = smap.to_rgba(val)
# make patches
patches = []
for i_c, i_clm in enumerate(clm):
patches.append(
Rectangle((i_clm[0], i_clm[2]), i_clm[1] - i_clm[0],
i_clm[3] - i_clm[2]))
patches_colle = PatchCollection(patches)
patches_colle.set_edgecolor('face')
patches_colle.set_facecolor(bin_colors)
return patches_colle, smap
def update(self, frame):
color = (0, 0, 0)
p = PatchCollection([self.__array_polygons[frame]], alpha=.5)
p.set_color(color)
p.set_edgecolor([0, 0, 0])
self.__ax.add_collection(p)
return
def plot_ax2_rejected(ax2):
ax2.set_title('Points per\nrejected-cloud', pad=5, fontsize=SMALL_SIZE + 1)
ax2.set_xlim(-0.1, 2.1)
ax2.set_ylim(-0.1, 1.1)
x_coord = np.linspace(0.0, 2.0, num=nrejected + 1)
widths = x_coord[1:] - x_coord[:-1]
patches = []
for i in range(nrejected):
x_patch = x_coord[i]
patches.append(
mpatches.FancyBboxPatch(
(x_patch, 0.0),
widths[i],
1.0,
boxstyle=mpatches.BoxStyle("Round", pad=0.0,
rounding_size=0.2)))
add_label(ax2, (x_patch + 0.5 * widths[i], 0.5),
data_size[~ids_bool][i],
color='white')
if nrejected == 0:
patches.append(
mpatches.FancyBboxPatch(
(0.0, 0.0),
2.0,
1.0,
boxstyle=mpatches.BoxStyle("Round", pad=0.0,
rounding_size=0.2)))
add_label(ax2, (1.0, 0.5), 'None', color='black')
collection = PatchCollection(patches, alpha=1.0)
collection.set_edgecolor('k')
collection.set_facecolor(colors_all[~ids_bool])
ax2.add_collection(collection)
ax2.axis('off')
def make_transform_plot(self,
fig,
i,
j,
k,
xlim,
ylim,
patches,
value,
bar=True):
# plot a map of the transform value
cmap = plt.cm.plasma_r
ax = fig.add_subplot(i, j, k)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
LC = PatchCollection(patches, cmap=cmap)
LC.set_array(value)
LC.set_edgecolor('none')
ax.add_collection(LC)
if bar:
fig.colorbar(LC)
ax.set_aspect('equal')
ax.patch.set_color([0.5, 0.5, 0.5])
ax.set_xticks([])
ax.set_yticks([])
return ax
def plot_tree(self):
# 1: plot all datapoints
keys = self.db.keys()
data = np.asarray(self.db.query(keys))
# @todo change database info
column = {"key": 0, "x": 1, "y": 2}
if self.args.quadlevel:
column["quad"] = 3
data = data[data[:, column["quad"]] < self.args.quadlevel]
markersize = 0.5
if len(data) < 100:
markersize = 4
plt.plot(data[:, column["x"]],
data[:, column["y"]],
'ko',
markersize=markersize)
# 2: plot all boundingboxes/partitions
bboxes = self.kdtree.partitions()
patches = []
for b in bboxes[self.args.bbox_depth]:
rect = plt.Rectangle(b.lower_left(),
b.width(),
b.height(),
ec="none",
edgecolor='black')
patches.append(rect)
# 3: show range query
if self.args.range_query:
rq = bb.BoundingBox.from_matrix(np.matrix(self.args.range_query))
patches.append(
plt.Rectangle(rq.lower_left(), rq.width(), rq.height()))
rkeys = self.kdtree.rquery(rq)
NN = np.asarray(self.db.query(rkeys))
plt.plot(NN[:, column["x"]],
NN[:, column["y"]],
'rs',
markersize=markersize + 4)
for point in NN:
if rq.within(point[1], 0):
if rq.within(point[2], 1):
plt.plot([point[1]], [point[2]],
'bs',
markersize=markersize + 4)
colors = np.linspace(0, 100, len(patches) + 1)
collection = PatchCollection(patches, cmap=plt.cm.jet, alpha=0.3)
collection.set_edgecolor([0, 0, 0])
collection.set_array(np.array(colors))
self.ax.add_collection(collection)
def c(p):
# where p is list of primitives
# outputs a collecction of primitives. essentially takes union. can treat this as a new primitive
coll = PatchCollection(p)
coll.set_edgecolor('k')
coll.set_facecolor([0, 0, 0, 0])
# coll.set_alpha(0.5)
return coll
def grid_res(mesh_path,
circumpolar=True,
set_bound=False,
bound=None,
save=False,
fig_name=None):
# Plotting parameters
if circumpolar:
lat_max = -30 + 90
font_sizes = [30, 24, 20]
# Build triangular patches for each element
elements, patches = make_patches(mesh_path, circumpolar)
# Calculate the grid resolution for each element
values = []
for elm in elements:
values.append(sqrt(elm.area()) * 1e-3)
# Set up figure
if circumpolar:
fig = figure(figsize=(16, 12))
ax = fig.add_subplot(1, 1, 1, aspect='equal')
else:
fig = figure(figsize=(16, 8))
ax = fig.add_subplot(1, 1, 1)
# Set colours for patches and add them to plot
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values))
img.set_edgecolor('face')
ax.add_collection(img)
# Configure plot
if circumpolar:
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
axis('off')
else:
xlim([-180, 180])
ylim([-90, 90])
ax.get_xaxis().set_ticks(arange(-120, 120 + 1, 60))
ax.get_yaxis().set_ticks(arange(-60, 60 + 1, 30))
title('Horizontal grid resolution (km)', fontsize=font_sizes[0])
if set_bound:
cbar = colorbar(img, extend='max')
else:
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=font_sizes[2])
if set_bound:
img.set_clim(vmin=0, vmax=bound)
if save:
fig.savefig(fig_name)
else:
fig.show()
def plot_bwtemp(x_min, x_max, y_min, y_max, gs, cbaxes, cbar_ticks, letter):
# Set up a grey square to fill the background with land
x_reg, y_reg = meshgrid(linspace(x_min, x_max, num=100),
linspace(y_min, y_max, num=100))
land_square = zeros(shape(x_reg))
# Find bounds on variable in this region
var_min, var_max = get_min_max(bwtemp_old,
bwtemp_new,
x_min,
x_max,
y_min,
y_max,
cavity=False)
print 'Bounds on bottom temperature: ' + str(var_min) + ' ' + str(var_max)
# Old simulation
ax = subplot(gs[0, 0], aspect='equal')
# Start with land background
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
# Add ocean elements
img = PatchCollection(patches_all_old, cmap='jet')
img.set_array(array(bwtemp_old))
img.set_edgecolor('face')
img.set_clim(vmin=var_min, vmax=var_max)
ax.add_collection(img)
# Add ice shelf front contour lines
contours_old = LineCollection(contour_lines_old,
edgecolor='black',
linewidth=1)
ax.add_collection(contours_old)
xlim([x_min, x_max])
ylim([y_min, y_max])
ax.set_xticks([])
ax.set_yticks([])
# Variable title
title(letter + r') Bottom water temperature ($^{\circ}$C)',
fontsize=18,
loc='left')
# New simulation
ax = subplot(gs[0, 1], aspect='equal')
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(patches_all_new, cmap='jet')
img.set_array(array(bwtemp_new))
img.set_edgecolor('face')
img.set_clim(vmin=var_min, vmax=var_max)
ax.add_collection(img)
contours_new = LineCollection(contour_lines_new,
edgecolor='black',
linewidth=1)
ax.add_collection(contours_new)
xlim([x_min, x_max])
ylim([y_min, y_max])
ax.set_xticks([])
ax.set_yticks([])
# Colourbar
cbar = colorbar(img, cax=cbaxes, ticks=cbar_ticks)
def init_polygons(ax):
polygons = read.polygons()
poly_coll = PatchCollection(polygons, alpha=0.6)
poly_colors = plt.get_cmap('Pastel1')(np.arange(.10, 1.99, .24))
poly_coll.set_facecolors(poly_colors)
poly_coll.set_edgecolor('black')
ax.add_collection(poly_coll)
return poly_coll, poly_colors
def plot_polygons(self):
colors = 100 * np.random.rand(len(self.__array_polygons))
p = PatchCollection(self.__array_polygons, alpha=.5)
p.set_array(np.array(colors))
p.set_edgecolor([0, 0, 0])
self.__ax.set_xlim((0, self.__x_lim))
self.__ax.set_ylim((0, self.__y_lim))
self.__ax.set_title(self.__title)
self.__ax.add_collection(p)
plt.show()
def plot_quadtree(self):
qt_depth = max(self.quadtree.quads.keys())
patches = []
for q in self.quadtree.quads[qt_depth]:
rect = plt.Rectangle(q.lower_left(), q.width(), q.height())
patches.append(rect)
collection = PatchCollection(patches)
collection.set_edgecolor([0, 0, 0])
self.ax.add_collection(collection)
def update(self, element: Element, time: Timestamp, µcluster: MicroCluster,
clusters: Optional[list[Cluster]],
unclustered: Optional[Set[MicroCluster]],
eliminated: Optional[Set[MicroCluster]]):
max_density = max(
[µcluster.density(time) for µcluster in self.dyclee.all_µclusters])
if µcluster in self.patch_map:
self.patch_map[µcluster].remove()
patch_collection = PatchCollection([
Rectangle(
µcluster.centroid - µcluster.context.hyperbox_lengths / 2,
*µcluster.context.hyperbox_lengths)
])
patch_collection.set_edgecolor('w')
patch_collection.set_facecolor(
(0, 0, 0, 0.5 * µcluster.density(time) / max_density))
self.patch_map[µcluster] = patch_collection
self.ax.add_collection(patch_collection)
if clusters is not None:
for cluster in clusters:
for µcluster in cluster.µclusters:
self.patch_map[µcluster].set_edgecolor(
self.colour_manager.get_colour(cluster.label))
self.patch_map[µcluster].set_facecolor(
(0, 0, 0, 0.5 * µcluster.density(time) / max_density))
order = sorted(range(len(clusters)),
key=lambda i: clusters[i].label)
self.ax.legend([
Rectangle((0, 0),
1,
1,
facecolor='none',
edgecolor=self.colour_manager.get_colour(
clusters[i].label)) for i in order
], [clusters[i].label for i in order],
loc=self.legend_loc)
if unclustered is not None:
for µcluster in unclustered:
self.patch_map[µcluster].set_edgecolor(
self.colour_manager.get_colour(µcluster.label,
self.unclustered_opacity))
self.patch_map[µcluster].set_facecolor(
(0, 0, 0, 0.5 * µcluster.density(time) / max_density))
if eliminated is not None:
for µcluster in eliminated:
self.patch_map[µcluster].remove()
del self.patch_map[µcluster]
def plot_mask (mesh_path, circumpolar):
# Plotting parameters
if circumpolar:
lat_max = -30 + 90
# Build triangular patches for each element
elements, patches = make_patches(mesh_path, circumpolar)
# Read mask
f = open(mesh_path + 'ssrestoring_mask.out', 'r')
node_mask = []
for line in f:
node_mask.append(float(line))
f.close()
# Apply to elements
mask = []
for elm in elements:
# For the purposes of plotting, unmask the element if at least 2 of the
# 3 component nodes are unmasked
if sum([node_mask[elm.nodes[0].id], node_mask[elm.nodes[1].id], node_mask[elm.nodes[2].id]]) >= 2:
# Unmask this element
mask.append(1)
else:
# Mask this element
mask.append(0)
# Set up figure
fig = figure(figsize=(12, 9))
if circumpolar:
ax = fig.add_subplot(1,1,1, aspect='equal')
else:
ax = fig.add_subplot(1,1,1)
# Set colours for patches and add them to plot
img = PatchCollection(patches, cmap=jet)
img.set_array(array(mask))
img.set_edgecolor('face')
ax.add_collection(img)
# Configure plot
if circumpolar:
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
else:
xlim([-180, 180])
ylim([-90, 90])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
title('Restoring mask')
cbar = colorbar(img)
img.set_clim(vmin=0, vmax=1)
axis('off')
show()
def add_circles(self, ax, centers, radis):
circles = []
for c, r in zip(centers, radis):
circles.append(patches.Circle(tuple(c), r))
patch_collection = PatchCollection(circles)
patch_collection.set_facecolor("none")
patch_collection.set_edgecolor("blue")
patch_collection.set_linewidth(1.5)
patch_collection.set_linestyle("dashed")
ax.add_collection(patch_collection)
print "added %d circles" % len(circles)
def plot_polygons(self):
p = PatchCollection(self.__array_polygons, alpha=.5)
p.set_facecolor([0, 0, 0])
p.set_edgecolor([0, 0, 0])
self.__ax.set_xlim((0, self.__x_lim))
self.__ax.set_ylim((0, self.__y_lim))
self.__ax.set_title(self.__title)
self.__ax.add_collection(p)
plt.axis('off')
plt.title('')
plt.plot()
plt.savefig('Results/' + self.__title, bbox_inches='tight', pad_inches=0)
def paw_plot(sdata=None, sdata_bg=None, cdata=None, cmap=None, clim=None, edgecolor='#555555', bgcolor='#cccccc', legend=True, ax=None):
ax = ax if ax else plt.gca()
r = np.array([ 0, 1, 1, 1, 1, 1 ])
theta = np.array([ 0, 30, 75, 120, 165, 210 ]) * np.pi / 180.0
theta = np.array([ -5, 25, 70, 115, 160, 205 ]) * np.pi / 180.0
# theta = np.array([ 0, 30, 72, 114, 156, 198 ]) * np.pi / 180.0
if sdata is None:
sdata = np.array([4,1,1,1,1,1])*.08
if cdata is None:
cdata = np.ones(len(r))
if cmap is None:
cmap = 'magma'
if clim is None:
clim = [0,1]
xx = r * np.cos(theta)
yy = r * np.sin(theta)
patches = [ mpatch.Circle((x,y),rad) for (x,y,rad) in zip(xx,yy,np.sqrt(sdata)) ]
col = PatchCollection(patches, zorder=2)
col.set_array(cdata)
col.set_cmap(cmap)
col.set_edgecolor(edgecolor)
col.set_clim(vmin=clim[0], vmax=clim[1])
ax.add_patch(mpatch.Arc((0.0, 0.0), 2.0, 2.0, angle=0.0,
theta1=theta[1]*180.0/np.pi, theta2=theta[-1]*180.0/np.pi,
edgecolor=edgecolor,facecolor='none', zorder=0, linestyle='--'))
ax.add_collection(col)
if legend:
plt.colorbar(col)
if sdata_bg is not None:
patches = [ mpatch.Circle((x,y),rad) for (x,y,rad) in zip(xx,yy,np.sqrt(sdata_bg)) ]
col = PatchCollection(patches, zorder=1)
col.set_edgecolor(edgecolor)
col.set_facecolor(bgcolor)
ax.add_collection(col)
ax.axis('equal')
ax.axis('off')
ax.set_xlim([-1.2,1.2])
ax.set_ylim([-.8,1.35])
def draw1DColumn(ax,
x,
val,
thk,
width=30,
ztopo=0,
cmin=1,
cmax=1000,
cmap=None,
name=None,
textoffset=0.0):
"""Draw a 1D column (e.g., from a 1D inversion) on a given ax.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pygimli.mplviewer import draw1DColumn
>>> thk = [1, 2, 3, 4]
>>> val = thk
>>> fig, ax = plt.subplots()
>>> draw1DColumn(ax, 0.5, val, thk, width=0.1, cmin=1, cmax=4, name="VES")
<matplotlib.collections.PatchCollection object at ...>
>>> ax.set_ylim(-np.sum(thk), 0)
(-10, 0)
"""
z = -np.hstack((0., np.cumsum(thk), np.sum(thk) * 1.5)) + ztopo
recs = []
for i in range(len(val)):
recs.append(Rectangle((x - width / 2., z[i]), width, z[i + 1] - z[i]))
pp = PatchCollection(recs)
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if cmap is not None:
if isinstance(cmap, str):
pp.set_cmap(pg.mplviewer.cmapFromName(cmap))
else:
pp.set_cmap(cmap)
pp.set_norm(colors.LogNorm(cmin, cmax))
pp.set_array(np.array(val))
pp.set_clim(cmin, cmax)
if name:
ax.text(x + textoffset, ztopo, name, ha='center', va='bottom')
updateAxes_(ax)
return col
def draw_bboxes(bboxes, ax=None, color='red', linewidth=1, **kw):
if ax is None:
ax = plt.gca()
patches = [
mpatches.Rectangle((i[1], i[0]), i[3] - i[1], i[2] - i[0])
for i in bboxes
]
boxcoll = PatchCollection(patches, **kw)
boxcoll.set_facecolor('none')
boxcoll.set_edgecolor(color)
boxcoll.set_linewidth(linewidth)
ax.collections = []
ax.add_collection(boxcoll)
def get_shape_collections(data: Dict):
shapes = []
for points in data.values():
shapes.append(
Polygon(np.array([points['lons'], points['lats']]).T, closed=True))
collection = PatchCollection(shapes)
collection.set_facecolor('#eeeeee')
collection.set_edgecolor('black')
collection.set_linewidth(0.2)
return collection
def plot_depth (mesh_path, fig_name, circumpolar=True):
# Plotting parameters
if circumpolar:
lat_max = -30 + 90
font_sizes = [240, 192, 160]
else:
font_sizes = [30, 24, 20]
# Build triangular patches for each element
elements, patches = make_patches(mesh_path, circumpolar)
# Find the depth of each element
elm_depth = []
for elm in elements:
depth1 = (elm.nodes[0].find_bottom()).depth
depth2 = (elm.nodes[1].find_bottom()).depth
depth3 = (elm.nodes[2].find_bottom()).depth
elm_depth.append(mean(array([depth1, depth2, depth3])))
# Set up figure
if circumpolar:
fig = figure(figsize=(128, 96))
ax = fig.add_subplot(1,1,1, aspect='equal')
else:
fig = figure(figsize=(16, 8))
ax = fig.add_subplot(1,1,1)
# Set colours for patches and add them to plot
img = PatchCollection(patches, cmap=jet)
img.set_array(array(elm_depth))
img.set_edgecolor('face')
ax.add_collection(img)
# Configure plot
if circumpolar:
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
axis('off')
else:
xlim([-180, 180])
ylim([-90, 90])
ax.get_xaxis().set_ticks(arange(-120,120+1,60))
ax.get_yaxis().set_ticks(arange(-60,60+1,30))
title('Seafloor depth (m)', fontsize=font_sizes[0])
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=font_sizes[2])
img.set_clim(vmin=0, vmax=max(elm_depth))
savefig(fig_name)
def wedgeplt(x_axis, y_axis, label, x_i, y_i, rotor, tur_id, patches,
image_property, save_path):
"""
用于【单机位点】尾流影响扇区/测试扇区示意图绘制
:param x_axis: 全场机位x，平面经度坐标，list/series
:param y_axis: 全场机位y，平面经度坐标，list/series
:param label: 全场机位编号，list/series
:param x_i: 对应绘制扇区机位i，平面经度坐标x，float
:param y_i: 对应绘制扇区机位i，平面经度坐标y，float
:param rotor: 对应绘制扇区机位i的叶轮直径，list/series
:param tur_id: 对应绘制扇区机位i的机位编号，str
:param patches:存储楔形wedge
:param image_property: str, 尾流影响wake-influenced/测试自由流free-flow扇区
:param save_path: 存储路径
:return:
"""
fig = plt.figure(figsize=(12, 6), tight_layout=True)
ax1 = fig.add_subplot(1, 1, 1)
tursite = ax1.scatter(x_axis, y_axis, marker='^', s=20, label='Center')
ax1.set_title('{} Turbine {} Sector(s) Display'.format(
tur_id, image_property),
fontsize=20)
for i in range(0, len(x_axis)): # 标注机位编号
ax1.text(x_axis[i], y_axis[i], label[i])
p = PatchCollection(patches, alpha=0.6)
if image_property == 'wake-influenced':
# p.set_color(c='orange') # Set both the edgecolor and the facecolor.
p.set_edgecolor(c=None)
p.set_facecolor(c='orange')
legend_patch = mpatches.Patch(
color='orange',
label='wake-influenced sectors') # 创建特殊的artists，添加到图例中
elif image_property == 'free-flow':
# p.set_color(c='green')
p.set_edgecolor(c=None)
p.set_facecolor(c='green')
legend_patch = mpatches.Patch(color='green', label='free-flow sectors')
ax1.add_collection(p)
ax1.legend(handles=[tursite, legend_patch], fontsize=10,
loc='lower left') # 指定legend的位置
ax1.set_aspect('equal')
# fig.colorbar(p, ax=ax1)
fig.savefig(os.path.join(
save_path, '{}机位{}扇区示意图.png'.format(tur_id, str(image_property))),
transparent=True) # 保存背景为透明图片
# plt.show()
plt.close("all")
def plot_depth(mesh_path, fig_name, circumpolar=True):
# Plotting parameters
if circumpolar:
lat_max = -30 + 90
font_sizes = [240, 192, 160]
else:
font_sizes = [30, 24, 20]
# Build triangular patches for each element
elements, patches = make_patches(mesh_path, circumpolar)
# Find the depth of each element
elm_depth = []
for elm in elements:
depth1 = (elm.nodes[0].find_bottom()).depth
depth2 = (elm.nodes[1].find_bottom()).depth
depth3 = (elm.nodes[2].find_bottom()).depth
elm_depth.append(mean(array([depth1, depth2, depth3])))
# Set up figure
if circumpolar:
fig = figure(figsize=(128, 96))
ax = fig.add_subplot(1, 1, 1, aspect='equal')
else:
fig = figure(figsize=(16, 8))
ax = fig.add_subplot(1, 1, 1)
# Set colours for patches and add them to plot
img = PatchCollection(patches, cmap=jet)
img.set_array(array(elm_depth))
img.set_edgecolor('face')
ax.add_collection(img)
# Configure plot
if circumpolar:
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
axis('off')
else:
xlim([-180, 180])
ylim([-90, 90])
ax.get_xaxis().set_ticks(arange(-120, 120 + 1, 60))
ax.get_yaxis().set_ticks(arange(-60, 60 + 1, 30))
title('Seafloor depth (m)', fontsize=font_sizes[0])
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=font_sizes[2])
img.set_clim(vmin=0, vmax=max(elm_depth))
savefig(fig_name)
def zoneLines(self, edgecolour='black'): #was 'red'
"""
Set boundary colour for defined ESRI shapes
edgecolour -- HTML colour name for boundary
"""
pc2 = PatchCollection(self.patches, match_original=True)
pc2.set_facecolor('none')
pc2.set_edgecolor(edgecolour)
pc2.set_alpha(0.5) #5.0
pc2.set_linewidth(0.5)
pc2.set_zorder(25) # 500
sq2 = self.ax.add_collection(pc2)
def plot_num_layers (mesh_path, fig_name):
# Plotting parameters
circumpolar = True
lat_max = -30 + 90
font_sizes = [240, 192, 160]
# Build triangular patches for each element
elements, patches = make_patches(mesh_path, circumpolar)
# Calculate the number of layers for each element
num_layers = []
for elm in elements:
num_layers_elm = 0
# Count the number of layers for each node
for i in range(3):
node = elm.nodes[i]
num_layers_node = 1
# Iterate until we reach the bottom
while node.below is not None:
num_layers_node += 1
node = node.below
num_layers_elm = max(num_layers_elm, num_layers_node)
# Save the maximum number of layers across the 3 nodes
num_layers.append(num_layers_elm)
# Set up figure
fig = figure(figsize=(128, 96))
ax = fig.add_subplot(1,1,1, aspect='equal')
# Set colours for patches and add them to plot
img = PatchCollection(patches, cmap=jet)
img.set_array(array(num_layers))
img.set_edgecolor('face')
ax.add_collection(img)
# Configure plot
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
title('Ice shelf draft (m)', fontsize=font_sizes[0])
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=font_sizes[2])
img.set_clim(vmin=1, vmax=47)
axis('off')
savefig(fig_name)
def _get_fpt_ell_collection(dm, fpts, T_data, alpha, edgecolor):
ell_patches = []
for (x, y, a, c, d) in fpts: # Manually Calculated sqrtm(inv(A))
with catch_warnings():
simplefilter("ignore")
aIS = 1 / sqrt(a)
cIS = (c / sqrt(a) - c / sqrt(d)) / (a - d + eps(1))
dIS = 1 / sqrt(d)
transEll = Affine2D([(aIS, 0, x), (cIS, dIS, y), (0, 0, 1)])
unitCirc1 = Circle((0, 0), 1, transform=transEll)
ell_patches = [unitCirc1] + ell_patches
ellipse_collection = PatchCollection(ell_patches)
ellipse_collection.set_facecolor("none")
ellipse_collection.set_transform(T_data)
ellipse_collection.set_alpha(alpha)
ellipse_collection.set_edgecolor(edgecolor)
return ellipse_collection
def animate(i):
[c.remove() for c in ax.collections]
patches = []
for (x, y), w in np.ndenumerate(matricies[i]):
if w != 0.0:
patches.append(Rectangle([x - boxsize / 2, y - boxsize / 2],
boxsize, boxsize,
fc=colourmap(w),
ec='black'))
colors = np.logspace(10e-30, 1, 100, endpoint=True)
p = PatchCollection(patches)
p.set_edgecolor('black')
p.set_array(np.array(colors))
ax.add_collection(p)
return ax,
class ArrayDisplay:
"""
Display a top-town view of a telescope array
"""
def __init__(self, telx, tely, mirrorarea,
axes=None, title="Array", autoupdate=True):
patches = [Circle(xy=(x, y), radius=np.sqrt(a))
for x, y, a in zip(telx, tely, mirrorarea)]
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches)
self.telescopes.set_clim(0, 100)
self.telescopes.set_array(np.zeros(len(telx)))
self.telescopes.set_cmap('spectral_r')
self.telescopes.set_edgecolor('none')
self.axes = axes if axes is not None else plt.gca()
self.axes.add_collection(self.telescopes)
self.axes.set_aspect(1.0)
self.axes.set_title(title)
self.axes.set_xlim(-1000, 1000)
self.axes.set_ylim(-1000, 1000)
self.bar = plt.colorbar(self.telescopes)
self.bar.set_label("Value")
@property
def values(self):
"""An array containing a value per telescope"""
return self.telescopes.get_array()
@values.setter
def values(self, values):
""" set the telescope colors to display """
self.telescopes.set_array(values)
self._update()
def _update(self):
""" signal a redraw if necessary """
if self.autoupdate:
plt.draw()
def bwsalt (mesh_path, file_path, save=False, fig_name=None):
# Plotting parameters
lat_max = -30 + 90
circumpolar=True
# Build FESOM mesh
elements, patches = make_patches(mesh_path, circumpolar)
# Calculate annual average of bottom water temperature
file = Dataset(file_path, 'r')
data = mean(file.variables['salt'][:,:], axis=0)
file.close()
values = []
# Loop over elements
for elm in elements:
values_tmp = []
# For each component node, find the bottom index; average over
# all 3 such indices to get the value for this element
for node in elm.nodes:
id = node.find_bottom().id
values_tmp.append(data[id])
values.append(mean(values_tmp))
# Plot
fig = figure(figsize=(16,12))
ax = fig.add_subplot(1,1,1,aspect='equal')
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values))
img.set_edgecolor('face')
img.set_clim(vmin=34, vmax=35)
ax.add_collection(img)
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
axis('off')
title(r'Bottom water temperature ($^{\circ}$C), annual average', fontsize=30)
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=20)
if save:
fig.savefig(fig_name)
else:
fig.show()
def draw1DColumn(ax, x, val, thk, width=30, ztopo=0, cmin=1, cmax=1000,
cmap=None, name=None, textoffset=0.0):
"""Draw a 1D column (e.g., from a 1D inversion) on a given ax.
Examples
--------
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from pygimli.mplviewer import draw1DColumn
>>> thk = [1, 2, 3, 4]
>>> val = thk
>>> fig, ax = plt.subplots()
>>> draw1DColumn(ax, 0.5, val, thk, width=0.1, cmin=1, cmax=4, name="VES")
<matplotlib.collections.PatchCollection object at ...>
>>> ax.set_ylim(-np.sum(thk), 0)
(-10, 0)
"""
z = -np.hstack((0., np.cumsum(thk), np.sum(thk) * 1.5)) + ztopo
recs = []
for i in range(len(val)):
recs.append(Rectangle((x - width / 2., z[i]), width, z[i + 1] - z[i]))
pp = PatchCollection(recs)
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if cmap is not None:
if isinstance(cmap, str):
pp.set_cmap(pg.mplviewer.cmapFromName(cmap))
else:
pp.set_cmap(cmap)
pp.set_norm(colors.LogNorm(cmin, cmax))
pp.set_array(np.array(val))
pp.set_clim(cmin, cmax)
if name:
ax.text(x+textoffset, ztopo, name, ha='center', va='bottom')
updateAxes_(ax)
return col
def plot_shelf (mesh_path, fig_name):
# Plotting parameters
circumpolar = True
lat_max = -30 + 90
font_sizes = [240, 192, 160]
# Build triangular patches for each element
elements, patches = make_patches(mesh_path, circumpolar)
# Find the ice shelf draft at each element
# (i.e. depth of surface nodes in ice shelf cavities)
elm_shelf = []
for elm in elements:
if elm.cavity:
shelf1 = (elm.nodes[0]).depth
shelf2 = (elm.nodes[1]).depth
shelf3 = (elm.nodes[2]).depth
elm_shelf.append(mean(array([shelf1, shelf2, shelf3])))
else:
elm_shelf.append(0.0)
# Set up figure
fig = figure(figsize=(128, 96))
ax = fig.add_subplot(1,1,1, aspect='equal')
# Set colours for patches and add them to plot
img = PatchCollection(patches, cmap=jet)
img.set_array(array(elm_shelf))
img.set_edgecolor('face')
ax.add_collection(img)
# Configure plot
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
title('Ice shelf draft (m)', fontsize=font_sizes[0])
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=font_sizes[2])
axis('off')
savefig(fig_name)
def plotGeometry(self, ax = None):
if (ax is None):
fig = plt.figure()
ax = fig.add_subplot(111)
ax.set_aspect(1)
self.blockPatch.set_facecolor('#F0F0F0')
self.blockPatch.set_edgecolor('k')
ax.add_patch(self.blockPatch)
pc = PatchCollection(self.channelPatches, match_original = True)
pc.set_facecolor('w')
pc.set_edgecolor('k')
pc.set_zorder(2)
ax.add_collection(pc)
self.plotDimensions(ax)
ax.set_xlim(self.xMin, self.xMax)
ax.set_ylim(self.yMin, self.yMax)
plt.show()
def show_box(b, col="b", ecol="k", alpha=1):
patches = []
# lower left corner at b[0], followed by width and height
xy = b[0]
width = np.absolute(b[0, 1] - b[0, 0])
height = np.absolute(b[1, 1] - b[1, 0])
art = mpatches.Rectangle(xy, width, height)
# art = mpatches.Rectangle(b[0],b[1,0],b[1,1])
patches.append(art)
ax = plt.gca()
ax.hold(True)
collection = PatchCollection(patches)
collection.set_facecolor(col)
collection.set_edgecolor(ecol)
collection.set_alpha(alpha)
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
plt.show()
def show_boxes(boxes, S=None, col="b", ecol="k", alpha=1):
if boxes.dim != 2:
raise Exception("show_boxes: dimension must be 2")
if S is None:
S = range(boxes.size)
patches = []
for i in S:
art = mpatches.Rectangle(boxes.corners[i], boxes.widths[i][0], boxes.widths[i][1])
patches.append(art)
ax = plt.gca()
ax.hold(True)
collection = PatchCollection(patches)
collection.set_facecolor(col)
collection.set_edgecolor(ecol)
collection.set_alpha(alpha)
ax.add_collection(collection, autolim=True)
ax.autoscale_view()
plt.show()
def make_plot(self, val, cct, clm,
cmap = 'jet', cmap_norm = None,
cmap_vmin = None, cmap_vmax = None):
# make color mapping
smap = ScalarMappable(cmap_norm, cmap)
smap.set_clim(cmap_vmin, cmap_vmax)
smap.set_array(val)
bin_colors = smap.to_rgba(val)
# make patches
patches = []
for i_c, i_clm in enumerate(clm):
patches.append(Rectangle((i_clm[0], i_clm[2]),
i_clm[1] - i_clm[0],
i_clm[3] - i_clm[2]))
patches_colle = PatchCollection(patches)
patches_colle.set_edgecolor('face')
patches_colle.set_facecolor(bin_colors)
return patches_colle, smap
def show_box(b,col='b',ecol='k',alpha=1, fig=None):
patches = []
# lower left corner at b[0], followed by width and height
art = mpatches.Rectangle(b[0],b[1,0],b[1,1])
patches.append(art)
if fig:
ax = fig.gca()
else:
fig = plt.figure()
ax = fig.gca()
ax.hold(True)
collection = PatchCollection(patches)
collection.set_facecolor(col)
collection.set_edgecolor(ecol)
collection.set_alpha(alpha)
ax.add_collection(collection,autolim=True)
ax.autoscale_view()
plt.show()
return fig
def plot_cavityflag (mesh_path, fig_name):
# Plotting parameters
circumpolar = True
lat_max = -30 + 90
font_sizes = [240, 192, 160]
# Build triangular patches for each element
elements, patches = make_patches(mesh_path, circumpolar)
# For each element, get the cavity flag
cavity = []
for elm in elements:
if elm.cavity:
cavity.append(1)
else:
cavity.append(0)
# Set up figure
fig = figure(figsize=(128, 96))
ax = fig.add_subplot(1,1,1, aspect='equal')
# Set colours for patches and add them to plot
img = PatchCollection(patches, cmap=jet)
img.set_array(array(cavity))
img.set_edgecolor('face')
ax.add_collection(img)
# Configure plot
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
title('Ice shelf cavity flag', fontsize=font_sizes[0])
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=font_sizes[2])
img.set_clim(vmin=0, vmax=1)
axis('off')
savefig(fig_name)
def PlotCalibration(group, cal, mask):
CalculateDIFC(InputWorkspace=group, CalibrationWorkspace=cal, OutputWorkspace='A')
CalculateDIFC(InputWorkspace=group, OutputWorkspace='B')
offset = 1 - mtd['A']/mtd['B']
#Accept either name or pointer to mask
if isinstance(mask, six.string_types):
mask = mtd[mask]
#Calculate angles theta and phi from detector position.
#Separate masked and unmasked detectors for plotting.
#Offset values of unma]sked detectors are stored for plotting
theta_array = []
phi_array = []
value_array = []
masked_theta_array = []
masked_phi_array = []
info = offset.spectrumInfo()
for idx, x in enumerate(info):
pos = x.position
theta = np.arccos(pos[2] / pos.norm())
phi = np.arctan2(pos[1], pos[0])
if mask.dataY(idx):
masked_theta_array.append(theta)
masked_phi_array.append(phi)
else:
theta_array.append(theta)
phi_array.append(phi)
value_array.append(np.sum(offset.dataY(idx)))
#Use the largest solid angle for circle radius
sample_position = info.samplePosition()
maximum_solid_angle = 0.0
for idx in six.moves.xrange(info.size()):
maximum_solid_angle = max(maximum_solid_angle, offset.getDetector(idx).solidAngle(sample_position))
#Radius also includes a fudge factor to improve plotting.
#May need to add finer adjustments on a per-instrument basis.
#Small circles seem to alias less than rectangles.
radius = maximum_solid_angle*8.0
patches = []
for x1, y1 in six.moves.zip(theta_array, phi_array):
circle = Circle((x1, y1), radius)
patches.append(circle)
masked_patches = []
for x1, y1 in six.moves.zip(masked_theta_array, masked_phi_array):
circle = Circle((x1, y1), radius)
masked_patches.append(circle)
#Matplotlib requires this to be a Numpy array.
colors = np.array(value_array)
p = PatchCollection(patches)
p.set_array(colors)
p.set_clim(-0.1,0.1)
p.set_edgecolor('face')
fig, ax = plt.subplots()
ax.add_collection(p)
mp = PatchCollection(masked_patches)
mp.set_facecolor('gray')
mp.set_edgecolor('face')
ax.add_collection(mp)
fig.colorbar(p, ax=ax)
ax.set_xlabel(r'$\phi$')
ax.set_xlim(0.0,np.pi)
ax.set_ylabel(r'$\theta$')
ax.set_ylim(-np.pi,np.pi)
return fig, ax
def lonlat_plot(
mesh_path,
file_path,
var_name,
depth_key,
depth,
depth_bounds,
tstep,
circumpolar,
elements,
patches,
mask_cavities=False,
save=False,
fig_name=None,
set_limits=False,
limits=None,
):
# Set bounds for domain
if circumpolar:
# Northern boundary 30S
lat_max = -30 + 90
else:
lon_min = -180
lon_max = 180
lat_min = -90
lat_max = 90
# Configure position of latitude and longitude labels
lon_ticks = arange(-120, 120 + 1, 60)
lat_ticks = arange(-60, 60 + 1, 30)
# Set font sizes
font_sizes = [30, 24, 20]
# Seconds per year, for conversion of ice shelf melt rate
sec_per_year = 365 * 24 * 3600
# Read data
file = Dataset(file_path, "r")
varid = file.variables[var_name]
data = varid[tstep - 1, :]
# Check for vector variables that need to be unrotated
if var_name in [
"uwind",
"vwind",
"stress_x",
"stress_y",
"uhice",
"vhice",
"uhsnow",
"vhsnow",
"uice",
"vice",
"u",
"v",
]:
# Read the rotated lat and lon
if var_name in ["u", "v"]:
# 3D variable
mesh_file = mesh_path + "nod3d.out"
else:
# 2D variable
mesh_file = mesh_path + "nod2d.out"
fid = open(mesh_file, "r")
fid.readline()
lon = []
lat = []
for line in fid:
tmp = line.split()
lon_tmp = float(tmp[1])
lat_tmp = float(tmp[2])
if lon_tmp < -180:
lon_tmp += 360
elif lon_tmp > 180:
lon_tmp -= 360
lon.append(lon_tmp)
lat.append(lat_tmp)
fid.close()
lon = array(lon)
lat = array(lat)
if var_name in ["uwind", "stress_x", "uhice", "uhsnow", "uice", "u"]:
# u-variable
u_data = data[:]
if var_name == "stress_x":
v_data = file.variables["stress_y"][tstep - 1, :]
else:
v_data = file.variables[var_name.replace("u", "v")][tstep - 1, :]
u_data_lonlat, v_data_lonlat = unrotate_vector(lon, lat, u_data, v_data)
data = u_data_lonlat[:]
elif var_name in ["vwind", "stress_y", "vhice", "vhsnow", "vice", "v"]:
# v-variable
v_data = data[:]
if var_name == "stress_y":
u_data = file.variables["stress_x"][tstep - 1, :]
else:
u_data = file.variables[var_name.replace("v", "u")][tstep - 1, :]
u_data_lonlat, v_data_lonlat = unrotate_vector(lon, lat, u_data, v_data)
data = v_data_lonlat[:]
# Set descriptive variable name and units for title
if var_name == "area":
name = "ice concentration"
units = "fraction"
elif var_name == "wnet":
# Convert from m/s to m/y
data = data * sec_per_year
name = "ice shelf melt rate"
units = "m/y"
else:
name = varid.getncattr("description")
units = varid.getncattr("units")
if depth_key == 0:
if var_name in ["temp", "salt", "u", "v"]:
depth_string = "at surface"
else:
depth_string = ""
elif depth_key == 1:
depth_string = "at bottom"
elif depth_key == 2:
depth_string = "vertically averaged"
elif depth_key == 3:
depth_string = "at " + str(depth) + " m"
elif depth_key == 4:
depth_string = "vertically averaged between " + str(depth_bounds[0]) + " and " + str(depth_bounds[1]) + " m"
# Build an array of data values corresponding to each Element
values = []
plot_patches = []
for elm in elements:
# If mask_cavities is true, only include elements which are not inside
# an ice shelf cavity; otherwise, include all elements
if (mask_cavities and not elm.cavity) or (not mask_cavities):
if depth_key == 0:
# Surface nodes; this is easy
# Average the data value for each of the three component nodes
values.append(mean([data[elm.nodes[0].id], data[elm.nodes[1].id], data[elm.nodes[2].id]]))
elif depth_key == 1:
# Bottom nodes
values_tmp = []
# For each of the three component nodes, find the id of the
# bottom node beneath it
for node in elm.nodes:
id = node.find_bottom().id
values_tmp.append(data[id])
# Average over these three values
values.append(mean(values_tmp))
elif depth_key == 2:
# Vertical average throughout entire water column
# First calculate volume: area of triangular face * water
# column thickness (mean of three corners)
wct = []
for node in elm.nodes:
wct.append(abs(node.find_bottom().depth - node.depth))
area = elm.area()
volume = area * mean(array(wct))
# Now integrate down
integral = 0
nodes = [elm.nodes[0], elm.nodes[1], elm.nodes[2]]
while True:
if nodes[0].below is None or nodes[1].below is None or nodes[2].below is None:
break
# Calculate mean of data at six corners of this triangular
# prism, and mean depths at three edges
values_tmp = []
dz_tmp = []
# Must loop over indices of nodes array, not entries,
# because we want to change the contents of the nodes array
for i in range(3):
values_tmp.append(data[nodes[i].id])
values_tmp.append(data[nodes[i].below.id])
dz_tmp.append(abs(nodes[i].depth - nodes[i].below.depth))
# Get ready for next iteration of the while loop
nodes[i] = nodes[i].below
# Integrand is mean of data at corners * area of upper face
# * mean of depths at edges
integral += mean(array(values_tmp)) * area * mean(array(dz_tmp))
# All done; divide integral by volume to get the average
values.append(integral / volume)
elif depth_key == 3:
# Specified depth
values_tmp = []
# For each of the three component nodes, linearly interpolate
# to the correct depth
for i in range(3):
# Find the ids of the nodes above and below this depth,
# and the coefficients for the linear interpolation
id1, id2, coeff1, coeff2 = elm.nodes[i].find_depth(depth)
if any(isnan(array([id1, id2, coeff1, coeff2]))):
# No such node at this depth
values_tmp.append(NaN)
else:
values_tmp.append(coeff1 * data[id1] + coeff2 * data[id2])
if any(isnan(array(values_tmp))):
pass
else:
values.append(mean(values_tmp))
coord = transpose(vstack((elm.x, elm.y)))
# Make new patches for elements which exist at this depth
plot_patches.append(Polygon(coord, True, linewidth=0.0))
elif depth_key == 4:
# Vertical average between two specified depths
shallow_bound = depth_bounds[0]
deep_bound = depth_bounds[1]
area = elm.area()
# Volume is area of triangular face * difference between
# depth bounds
volume = abs(deep_bound - shallow_bound) * area
nodes = [elm.nodes[0], elm.nodes[1], elm.nodes[2]]
# Check if we're already too deep
if nodes[0].depth > deep_bound or nodes[1].depth > deep_bound or nodes[2].depth > deep_bound:
pass
# Check if the seafloor is shallower than shallow_bound
elif (
nodes[0].find_bottom().depth <= shallow_bound
or nodes[1].find_bottom().depth <= shallow_bound
or nodes[2].find_bottom().depth <= shallow_bound
):
pass
else:
# Here we go
# Find the first 3D element which is entirely below
# shallow_bound
while True:
if (
nodes[0].depth > shallow_bound
and nodes[1].depth > shallow_bound
and nodes[2].depth > shallow_bound
):
# Save these nodes
first_nodes = []
for i in range(3):
first_nodes.append(nodes[i])
break
else:
for i in range(3):
nodes[i] = nodes[i].below
# Integrate downward until one of the next nodes hits the
# seafloor, or is deeper than deep_bound
integral = 0
while True:
if nodes[0].below is None or nodes[1].below is None or nodes[2].below is None:
# We've reached the seafloor
last_nodes = None
break
if (
nodes[0].below.depth > deep_bound
or nodes[1].below.depth > deep_bound
or nodes[2].below.depth > deep_bound
):
# At least one of the next nodes will pass
# deep_bound; save the current nodes
last_nodes = []
for i in range(3):
last_nodes.append(nodes[i])
break
else:
# Calculate mean of data at six corners of this
# triangular prism, and mean depths at three edges
values_tmp = []
dz_tmp = []
for i in range(3):
values_tmp.append(data[nodes[i].id])
values_tmp.append(data[nodes[i].below.id])
dz_tmp.append(abs(nodes[i].depth - nodes[i].below.depth))
# Get ready for next iteration of while loop
nodes[i] = nodes[i].below
# Integrand is mean of data at corners *
# area of upper face * mean of depths at edges
integral += mean(array(values_tmp)) * area * mean(array(dz_tmp))
# Now integrate from shallow_bound to first_nodes by
# linearly interpolating each node to shallow_bound
values_tmp = []
dz_tmp = []
for i in range(3):
values_tmp.append(data[first_nodes[i].id])
id1, id2, coeff1, coeff2 = elm.nodes[i].find_depth(shallow_bound)
if any(isnan(array([id1, id2, coeff1, coeff2]))):
# first_nodes[i] was the shallowest node, we can't
# interpolate above it
values_tmp.append(NaN)
else:
values_tmp.append(coeff1 * data[id1] + coeff2 * data[id2])
dz_tmp.append(abs(first_nodes[i].depth - shallow_bound))
if any(isnan(array(values_tmp))):
pass
else:
integral += mean(array(values_tmp)) * area * mean(array(dz_tmp))
# Now integrate from last_nodes to deep_bound by linearly
# interpolating each node to shallow_bound, unless we hit
# the seafloor earlier
if last_nodes is not None:
values_tmp = []
dz_tmp = []
for i in range(3):
values_tmp.append(data[last_nodes[i].id])
id1, id2, coeff1, coeff2 = elm.nodes[i].find_depth(deep_bound)
if any(isnan(array([id1, id2, coeff1, coeff2]))):
# last_nodes[i] was the deepest node, we can't
# interpolate below it
values_tmp.append(NaN)
else:
values_tmp.append(coeff1 * data[id1] + coeff2 * data[id2])
dz_tmp.append(abs(deep_bound - last_nodes[i].depth))
if any(isnan(array(values_tmp))):
pass
else:
integral += mean(array(values_tmp)) * area * mean(array(dz_tmp))
# All done; divide integral by volume to get the average
values.append(integral / volume)
# Make new patches for elements which exist at this depth
coord = transpose(vstack((elm.x, elm.y)))
plot_patches.append(Polygon(coord, True, linewidth=0.0))
if depth_key < 3:
# Use all patches
plot_patches = patches[:]
if mask_cavities:
# Get mask array of patches for ice shelf cavity elements
mask_patches = iceshelf_mask(elements)
if var_name == "wnet":
# Swap with regular patches so that open ocean elements are masked,
# ice shelf cavity nodes are not
tmp = plot_patches
plot_patches = mask_patches
mask_patches = tmp
# Choose colour bounds
if set_limits:
# User-specified bounds
var_min = limits[0]
var_max = limits[1]
if var_min == -var_max:
# Bounds are centered on zero, so choose a blue-to-red colourmap
# centered on yellow
colour_map = "RdYlBu_r"
else:
colour_map = "jet"
else:
# Determine bounds automatically
if var_name in [
"uwind",
"vwind",
"qnet",
"olat",
"osen",
"wnet",
"virtual_salt",
"relax_salt",
"stress_x",
"stress_y",
"uice",
"vice",
"u",
"v",
"w",
]:
# Center levels on 0 for certain variables, with a blue-to-red
# colourmap
max_val = amax(abs(array(values)))
var_min = -max_val
var_max = max_val
colour_map = "RdYlBu_r"
else:
var_min = amin(array(values))
var_max = amax(array(values))
colour_map = "jet"
# Set up plot
if circumpolar:
fig = figure(figsize=(16, 12))
ax = fig.add_subplot(1, 1, 1, aspect="equal")
else:
fig = figure(figsize=(16, 8))
ax = fig.add_subplot(1, 1, 1)
# Set colourmap for patches, and refer it to the values array
img = PatchCollection(plot_patches, cmap=colour_map)
img.set_array(array(values))
img.set_edgecolor("face")
# Add patches to plot
ax.add_collection(img)
if mask_cavities:
# Set colour to light grey for patches in mask
overlay = PatchCollection(mask_patches, facecolor=(0.6, 0.6, 0.6))
overlay.set_edgecolor("face")
# Add mask to plot
ax.add_collection(overlay)
# Configure plot
if circumpolar:
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
axis("off")
else:
xlim([lon_min, lon_max])
ylim([lat_min, lat_max])
xticks(lon_ticks)
yticks(lat_ticks)
xlabel("Longitude", fontsize=font_sizes[1])
ylabel("Latitude", fontsize=font_sizes[1])
setp(ax.get_xticklabels(), fontsize=font_sizes[2])
setp(ax.get_yticklabels(), fontsize=font_sizes[2])
title(name + " (" + units + ") " + depth_string, fontsize=font_sizes[0])
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=font_sizes[2])
img.set_clim(vmin=var_min, vmax=var_max)
# Plot specified points
# problem_ids = [16, 17, 36, 64, 67, 70, 160, 262, 266, 267, 268, 273, 274, 280, 283, 287, 288, 289, 290, 291, 292, 293, 294, 296, 297, 350, 351, 352, 353, 360, 479, 480, 493, 507, 508, 509, 521, 539, 547, 548, 549, 550, 554, 555]
# problem_x = []
# problem_y = []
# for elm in elements:
# for i in range(3):
# if elm.nodes[i].id in problem_ids:
# problem_x.append(elm.x[i])
# problem_y.append(elm.y[i])
# problem_ids.remove(elm.nodes[i].id)
# ax.plot(problem_x, problem_y, 'or')
if save:
fig.savefig(fig_name)
else:
fig.show()
def showStitchedModels(mods, axes=None, cmin=None, cmax=None, **kwargs):
"""
Show several 1d block models as (stitched) section.
"""
x = kwargs.pop('x', np.arange(len(mods)))
topo = kwargs.pop('topo', x*0)
nlay = int(np.floor((len(mods[0]) - 1) / 2.)) + 1
if cmin is None or cmax is None:
cmin = 1e9
cmax = 1e-9
for model in mods:
res = np.asarray(model)[nlay - 1:nlay * 2 - 1]
cmin = min(cmin, min(res))
cmax = max(cmax, max(res))
if kwargs.pop('sameSize', True): # all having the same width
dx = np.ones_like(x)*np.median(np.diff(x))
else:
dx = np.diff(x) * 1.05
dx = np.hstack((dx, dx[-1]))
x1 = x - dx / 2
if axes is None:
fig, ax = plt.subplots()
else:
ax = axes
fig = ax.figure
# ax.plot(x, x * 0., 'k.')
zm = kwargs.pop('zm', None)
maxz = 0.
if zm is not None:
maxz = zm
recs = []
RES = []
for i, mod in enumerate(mods):
mod1 = np.asarray(mod)
res = mod1[nlay - 1:]
RES.extend(res)
thk = mod1[:nlay - 1]
thk = np.hstack((thk, thk[-1]))
z = np.hstack((0., np.cumsum(thk)))
if zm is not None:
thk[-1] = zm - z[-2]
z[-1] = zm
else:
maxz = max(maxz, z[-1])
for j in range(len(thk)):
recs.append(Rectangle((x1[i], topo[i]-z[j]), dx[i], -thk[j]))
pp = PatchCollection(recs, edgecolors=kwargs.pop('edgecolors', 'none'))
pp.set_edgecolor(kwargs.pop('edgecolors', 'none'))
pp.set_linewidths(0.0)
ax.add_collection(pp)
if 'cmap' in kwargs:
pp.set_cmap(kwargs['cmap'])
print(cmin, cmax)
norm = LogNorm(cmin, cmax)
pp.set_norm(norm)
pp.set_array(np.array(RES))
# pp.set_clim(cmin, cmax)
ax.set_ylim((-maxz, max(topo)))
ax.set_xlim((x1[0], x1[-1] + dx[-1]))
cbar = None
if kwargs.pop('colorBar', True):
cbar = plt.colorbar(pp, ax=ax, norm=norm, orientation='horizontal',
aspect=60) # , ticks=[1, 3, 10, 30, 100, 300])
if 'ticks' in kwargs:
cbar.set_ticks(kwargs['ticks'])
# cbar.autoscale_None()
if axes is None: # newly created fig+ax
return fig, ax
else: # already given, better give back color bar
return cbar
def ismr_map (mesh_path, log_path, save=False, fig_name=None):
# Limits on longitude and latitude for each ice shelf
# These depend on the source geometry, in this case RTopo 1.05
# Note there is one extra index at the end of each array; this is because
# the Ross region crosses the line 180W and therefore is split into two
# We have -181 and 181 not -180 and 180 at this boundary so that
# elements which cross the boundary are still counted
lon_min = [-62.67, -65.5, -79.17, -85, -104.17, -102.5, -108.33, -114.5, -135.67, -149.17, -155, 144, 115, 94.17, 80.83, 65, 33.83, 19, 12.9, 9.33, -10.05, -28.33, -181, 158.33]
lon_max = [-59.33, -60, -66.67, -28.33, -88.83, -99.17, -103.33, -111.5, -114.33, -140, -145, 146.62, 123.33, 102.5, 89.17, 75, 37.67, 33.33, 16.17, 12.88, 7.6, -10.33, -146.67, 181]
lat_min = [-73.03, -69.35, -74.17, -83.5, -73.28, -75.5, -75.5, -75.33, -74.9, -76.42, -78, -67.83, -67.17, -66.67, -67.83, -73.67, -69.83, -71.67, -70.5, -70.75, -71.83, -76.33, -85, -84.5]
lat_max = [-69.37, -66.13, -69.5, -74.67, -71.67, -74.17, -74.67, -73.67, -73, -75.17, -76.41, -66.67, -66.5, -64.83, -66.17, -68.33, -68.67, -68.33, -69.33, -69.83, -69.33, -71.5, -77.77, -77]
# Area of each ice shelf in m^2 (printed to screen during
# timeseries_massloss.py, update if the mesh changes)
area = [9410595961.42, 48147893361.6, 46287951910.9, 429798928470.0, 27030080949.7, 3839594948.81, 2499220358.3, 3908582947.28, 29823059449.5, 4268520899.01, 11108310834.3, 3102730054.84, 4632897701.36, 26030138936.9, 11651566872.8, 64322690314.0, 2957848286.81, 40563562257.6, 6778604330.34, 5671169444.22, 52720412012.5, 72401508276.4, 475666675975.0]
# Observed melt rate (Rignot 2013) and uncertainty for each ice shelf, in Gt/y
obs_ismr = [0.1, 0.4, 3.1, 0.3, 1.7, 16.2, 17.7, 7.8, 4.3, 0.6, 1.5, 1.4, 7.7, 2.8, 1.7, 0.6, -0.4, 0.4, 0.7, 0.5, 0.5, 0.1, 0.1]
obs_ismr_error = [0.6, 1, 0.8, 0.1, 0.6, 1, 1, 0.6, 0.4, 0.3, 0.3, 0.6, 0.7, 0.6, 0.7, 0.4, 0.6, 0.4, 0.2, 0.2, 0.2, 0.2, 0.1]
# Density of ice in kg/m^3
rho_ice = 916
# Plotting parameters
max_lat_plot = -63+90
mask_cavities = True
circumpolar = True
# Assume timeseries are 5-day averages
days_per_output = 5
output_per_year = 365.0/days_per_output
# Build FESOM mesh
# Get separate patches for the open ocean elements so we can mask them out
elements, mask_patches = make_patches(mesh_path, circumpolar, mask_cavities)
patches = iceshelf_mask(elements)
# Read log file
f = open(log_path, 'r')
# Skip the first line (header)
f.readline()
# Count the number of time indices for the first variable (total mass loss
# for all ice shelves, which we don't care about)
num_time = 0
for line in f:
try:
tmp = float(line)
num_time += 1
except(ValueError):
# Reached header for first individual ice shelf
break
# Set up array for melt rate values at each ice shelf
ismr_ts = empty([len(obs_ismr), num_time])
index = 0
# Loop over ice shelves
while index < len(obs_ismr):
t = 0
for line in f:
try:
# Convert from mass loss to area-averaged melt rate
ismr_ts[index,t] = float(line)*1e12/(rho_ice*area[index])
t += 1
except(ValueError):
# Reached the header for the next ice shelf
break
index += 1
# Find unexplained error in annual average
ismr = empty(len(obs_ismr))
error_vals = empty(len(obs_ismr))
for index in range(len(obs_ismr)):
# Calculate annual average
ismr[index] = mean(ismr_ts[index, -output_per_year:])
# Calculate range of observations
ismr_low = obs_ismr[index] - obs_ismr_error[index]
ismr_high = obs_ismr[index] + obs_ismr_error[index]
# Calculate unexplained percent error in melt rate
if ismr[index] < ismr_low:
# Simulated melt rate too low
error_vals[index] = (ismr[index] - ismr_low)/ismr_low*100
elif ismr[index] > ismr_high:
# Simulated melt rate too high
error_vals[index] = (ismr[index] - ismr_high)/ismr_high*100
else:
# Simulated melt rate within observational error estimates
error_vals[index] = 0
# Scale for plotting
max_val = 100 #amax(abs(error_vals))
# Build a field of ice shelf melt rate unexplained percent error
values = []
# Keep track of which elements are in ice shelves we care about
location_flag = zeros(len(elements))
for i in range(len(elements)):
elm = elements[i]
# Make sure we're actually in an ice shelf cavity
if elm.cavity:
error_tmp = 0.0
# Loop over ice shelves
for index in range(len(obs_ismr)):
# Figure out whether or not this element is part of the given
# ice shelf
if all(elm.lon >= lon_min[index]) and all(elm.lon <= lon_max[index]) and all(elm.lat >= lat_min[index]) and all(elm.lat <= lat_max[index]):
location_flag[i] = 1
error_tmp = error_vals[index]
if index == len(obs_ismr)-1:
# Ross region is split in two
if all(elm.lon >= lon_min[index+1]) and all(elm.lon <= lon_max[index+1]) and all(elm.lat >= lat_min[index+1]) and all(elm.lat <= lat_max[index+1]):
location_flag[i] = 1
error_tmp = error_vals[index]
values.append(error_tmp)
# Set up a grey square covering the domain, anything that isn't covered
# up later is land
x_reg, y_reg = meshgrid(linspace(-lat_max, lat_max, num=100), linspace(-lat_max, lat_max, num=100))
land_square = zeros(shape(x_reg))
# Plot
fig = figure(figsize=(16,12))
ax = fig.add_subplot(1,1,1,aspect='equal')
# Start with grey square background for land
contourf(x_reg, y_reg, land_square, 1, colors=(('0.6', '0.6', '0.6')))
img = PatchCollection(patches, cmap='RdBu_r')
img.set_array(array(values))
img.set_edgecolor('face')
img.set_clim(-max_val, max_val)
ax.add_collection(img)
# Mask out the open ocean in white
overlay = PatchCollection(mask_patches, facecolor=(1,1,1))
overlay.set_edgecolor('face')
ax.add_collection(overlay)
# Contour ice shelf front
contour_lines = []
for elm in elements:
# Select elements where exactly 2 of the 3 nodes are in a cavity
if count_nonzero(elm.cavity_nodes) == 2:
# Save the coastal flags and x- and y- coordinates of these 2
coast_tmp = []
x_tmp = []
y_tmp = []
for i in range(3):
if elm.cavity_nodes[i]:
coast_tmp.append(elm.coast_nodes[i])
x_tmp.append(elm.x[i])
y_tmp.append(elm.y[i])
# Select elements where at most 1 of these 2 nodes are coastal
if count_nonzero(coast_tmp) < 2:
# Draw a line between the 2 nodes
contour_lines.append([(x_tmp[0], y_tmp[0]), (x_tmp[1], y_tmp[1])])
# Add all the lines to the plot
contours = LineCollection(contour_lines, edgecolor='black', linewidth=1)
ax.add_collection(contours)
# Configure plot
xlim([-max_lat_plot, max_lat_plot])
ylim([-max_lat_plot, max_lat_plot])
axis('off')
title('Bias in Ice Shelf Melt Rate (%)', fontsize=30)
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=20)
# Finished
if save:
fig.savefig(fig_name)
else:
fig.show()
def patchValMap(vals, xvec=None, yvec=None, ax=None, cMin=None, cMax=None,
logScale=None, label=None, dx=1, dy=None, **kwargs):
""" plot previously generated (generateVecMatrix) y map (category)
Parameters
----------
A : iterable
to show
xvec : dict {i:num}
dict (must match A.shape[0])
ymap : iterable
vector for x axis (must match A.shape[0])
ax : mpl.axis
axis to plot, if not given a new figure is created
cMin/cMax : float
minimum/maximum color values
logScale : bool
logarithmic colour scale [min(A)>0]
label : string
colorbar label
"""
if cMin is None:
cMin = np.min(vals)
if cMax is None:
cMax = np.max(vals)
if logScale is None:
logScale = (cMin > 0.0)
if logScale:
norm = LogNorm(vmin=cMin, vmax=cMax)
else:
norm = Normalize(vmin=cMin, vmax=cMax)
if 'ax' is None:
fig, ax = plt.subplots()
recs = []
if dy is None: # map y values to unique
ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))}
for i in range(len(vals)):
recs.append(Rectangle((xvec[i]-dx/2, ymap[yvec[i]]-0.5), dx, 1))
else:
for i in range(len(vals)):
recs.append(Rectangle((xvec[i]-dx/2, yvec[i]-dy/2), dx, dy))
pp = PatchCollection(recs)
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if 'cmap' in kwargs:
pp.set_cmap(kwargs.pop('cmap'))
pp.set_norm(norm)
pp.set_array(np.array(vals))
pp.set_clim(cMin, cMax)
ax.set_xlim(min(xvec)-dx/2, max(xvec)+dx/2)
ax.set_ylim(len(ymap)-0.5, -0.5)
updateAxes_(ax)
cbar = None
if kwargs.pop('colorBar', True):
cbar = pg.mplviewer.createColorbar(col, cMin=cMin, cMax=cMax, nLevs=5,
label=label)
return ax, cbar, ymap
def patchValMap(vals, xvec=None, yvec=None, ax=None, cMin=None, cMax=None,
logScale=None, label=None, dx=1, dy=None, **kwargs):
"""Plot previously generated (generateVecMatrix) y map (category).
Parameters
----------
vals : iterable
Data values to show.
xvec : dict {i:num}
dict (must match vals.shape[0])
ymap : iterable
vector for x axis (must match vals.shape[0])
ax : mpl.axis
axis to plot, if not given a new figure is created
cMin/cMax : float
minimum/maximum color values
logScale : bool
logarithmic colour scale [min(vals)>0]
label : string
colorbar label
** kwargs:
* circular : bool
Plot in polar coordinates.
"""
if cMin is None:
cMin = np.min(vals)
if cMax is None:
cMax = np.max(vals)
if logScale is None:
logScale = (cMin > 0.0)
norm = None
if logScale and cMin > 0:
norm = LogNorm(vmin=cMin, vmax=cMax)
else:
norm = Normalize(vmin=cMin, vmax=cMax)
if ax is None:
ax = plt.subplots()[1]
recs = []
circular = kwargs.pop('circular', False)
if circular:
recs = [None] * len(xvec)
if dy is None: # map y values to unique
ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))}
xyMap = {}
for i, y in enumerate(yvec):
if y not in xyMap:
xyMap[y] = []
xyMap[y].append(i)
# maxR = max(ymap.values()) # what's that for? not used
dR = 1 / (len(ymap.values())+1)
# dOff = np.pi / 2 # what's that for? not used
for y, xIds in xyMap.items():
r = 1. - dR*(ymap[y]+1)
# ax.plot(r * np.cos(xvec[xIds]),
# r * np.sin(xvec[xIds]), 'o')
# print(y, ymap[y])
for i in xIds:
phi = xvec[i]
# x = r * np.cos(phi) # what's that for? not used
y = r * np.sin(phi)
dPhi = (xvec[1] - xvec[0])
recs[i] = Wedge((0., 0.), r + dR/1.5,
(phi - dPhi)*360/(2*np.pi),
(phi + dPhi)*360/(2*np.pi),
width=dR,
zorder=1+r)
# if i < 5:
# ax.text(x, y, str(i))
# pg.wait()
else:
raise("Implementme")
else:
if dy is None: # map y values to unique
ymap = {xy: ii for ii, xy in enumerate(np.unique(yvec))}
for i in range(len(vals)):
recs.append(Rectangle((xvec[i] - dx / 2, ymap[yvec[i]] - 0.5),
dx, 1))
else:
for i in range(len(vals)):
recs.append(Rectangle((xvec[i] - dx / 2, yvec[i] - dy / 2),
dx, dy))
ax.set_xlim(min(xvec) - dx / 2, max(xvec) + dx / 2)
ax.set_ylim(len(ymap) - 0.5, -0.5)
pp = PatchCollection(recs)
# ax.clear()
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if circular:
pp.set_edgecolor('black')
pp.set_linewidths(0.1)
cmap = pg.mplviewer.cmapFromName(**kwargs)
if kwargs.pop('markOutside', False):
cmap.set_bad('grey')
cmap.set_under('darkgrey')
cmap.set_over('lightgrey')
cmap.set_bad('black')
pp.set_cmap(cmap)
pp.set_norm(norm)
pp.set_array(vals)
pp.set_clim(cMin, cMax)
updateAxes_(ax)
cbar = kwargs.pop('colorBar', True)
ori = kwargs.pop('orientation', 'horizontal')
if cbar in ['horizontal', 'vertical']:
ori = cbar
cbar = True
if cbar is True: # not for cbar=1, which is really confusing!
cbar = pg.mplviewer.createColorBar(col, cMin=cMin, cMax=cMax,
nLevs=5, label=label,
orientation=ori)
elif cbar is not False:
# .. cbar is an already existing cbar .. so we update its values
pg.mplviewer.updateColorBar(cbar, cMin=cMin, cMax=cMax,
nLevs=5, label=label)
updateAxes_(ax)
return ax, cbar, ymap
def nsidc_aice_seasonal (mesh_path, file_path1, file_path2, save=False, fig_name=None):
# FESOM parameters
circumpolar=True
mask_cavities=True
# Season names for plot titles
season_names = ['DJF', 'MAM', 'JJA', 'SON']
# NSIDC file paths
nsidc_head = '/short/m68/kaa561/nsidc_aice/seaice_conc_monthly_sh'
nsidc_head_0 = nsidc_head + '_f11_'
nsidc_head_1 = nsidc_head + '_f13_'
nsidc_tail = '_v02r00.nc'
# Degrees to radians conversion factor
deg2rad = pi/180.0
# Number of days per month (just for CICE)
ndays_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
# Build FESOM mesh
elements, patches = make_patches(mesh_path, circumpolar, mask_cavities)
# Get seasonal averages of the FESOM output
# This is hard-coded and ugly
id = Dataset(file_path1, 'r')
n2d = id.variables['area'].shape[1]
fesom_data = zeros([4, n2d])
# DJF: 1/5 of index 67 (1-based) and indices 68-73 in file1; indices 1-11
# and 4/5 of index 12 in file2; 90 days in total
fesom_data[0,:] = id.variables['area'][66,:] + sum(id.variables['area'][67:73,:]*5, axis=0)
id.close()
id = Dataset(file_path2, 'r')
fesom_data[0,:] += sum(id.variables['area'][0:11,:]*5, axis=0) + id.variables['area'][11,:]*4
fesom_data[0,:] /= 90
# MAM: 1/5 of index 12, indices 13-30, and 1/5 of index 31 in file2;
# 92 days in total
fesom_data[1,:] = id.variables['area'][11,:] + sum(id.variables['area'][12:30,:]*5, axis=0) + id.variables['area'][30,:]
fesom_data[1,:] /= 92
# JJA: 4/5 of index 31, indices 32-48, and 3/5 of index 49 in file2;
# 92 days in total
fesom_data[2,:] = id.variables['area'][30,:]*4 + sum(id.variables['area'][31:48]*5, axis=0) + id.variables['area'][48,:]*3
fesom_data[2,:] /= 92
# SON: 2/5 of index 49, indices 50-66, and 4/5 of index 67 in file2;
# 91 days in total
fesom_data[3,:] = id.variables['area'][48,:]*2 + sum(id.variables['area'][49:66,:]*5, axis=0) + id.variables['area'][66,:]*4
fesom_data[3,:] /= 91
id.close()
# Read NSIDC grid from the January file
id = Dataset(nsidc_head_0 + '199501' + nsidc_tail, 'r')
nsidc_lon = id.variables['longitude'][:,:]
nsidc_lat = id.variables['latitude'][:,:]
id.close()
# Initialise seasonal averages of NSIDC data
nsidc_data = ma.empty([4, size(nsidc_lon,0), size(nsidc_lon,1)])
nsidc_data[:,:] = 0.0
# Process one season at a time
for season in range(4):
# Figure out which months we care about
if season == 0:
# DJF
nsidc_months = [12, 1, 2]
elif season == 1:
# MAM
nsidc_months = [3, 4, 5]
elif season == 2:
# JJA
nsidc_months = [6, 7, 8]
elif season == 3:
# SON
nsidc_months = [9, 10, 11]
season_days = 0 # Number of days in season; this will be incremented
# Process one month at a time
for month in nsidc_months:
# Construct NSIDC file path
if month < 10:
nsidc_file = nsidc_head_0 + '19950' + str(month) + nsidc_tail
else:
nsidc_file = nsidc_head_1 + '1995' + str(month) + nsidc_tail
# Read concentration data
id = Dataset(nsidc_file, 'r')
nsidc_data_raw = id.variables['seaice_conc_monthly_cdr'][0,:,:]
# Read std just for the mask
nsidc_mask = id.variables['stdev_of_seaice_conc_monthly_cdr'][0,:,:]
id.close()
# Set land mask
nsidc_data_tmp = ma.empty(shape(nsidc_data_raw))
nsidc_data_tmp[:,:] = 0.0
nsidc_data_tmp[~nsidc_mask.mask] = nsidc_data_raw[~nsidc_mask.mask]
nsidc_data_tmp[nsidc_mask.mask] = ma.masked
# Accumulate master array, weighted with number of days per month
nsidc_data[season,:,:] += nsidc_data_tmp*ndays_month[month-1]
season_days += ndays_month[month-1]
# Convert from sum to average
nsidc_data[season,:,:] /= season_days
# Convert to spherical coordinates
nsidc_x = -(nsidc_lat+90)*cos(nsidc_lon*deg2rad+pi/2)
nsidc_y = (nsidc_lat+90)*sin(nsidc_lon*deg2rad+pi/2)
# Find boundaries for each side of plot based on extent of NSIDC grid
bdry1 = amax(nsidc_x[:,0])
bdry2 = amin(nsidc_x[:,-1])
bdry3 = amin(nsidc_y[:,0])
bdry4 = amax(nsidc_y[:,-1])
# Set consistent colour levels
lev = linspace(0, 1, num=50)
# Plot
fig = figure(figsize=(20,9))
# Loop over seasons
for season in range(4):
# NSIDC
ax = fig.add_subplot(2, 4, season+1, aspect='equal')
contourf(nsidc_x, nsidc_y, nsidc_data[season,:,:], lev)
if season == 0:
text(-39, 0, 'NSIDC', fontsize=24, ha='right')
title(season_names[season], fontsize=24)
xlim([bdry1, bdry2])
ylim([bdry3, bdry4])
axis('off')
# Build an array of FESOM data values corresponding to each Element
values = []
for elm in elements:
# For each element not in an ice shelf cavity, append the mean
# value for the 3 component Nodes
if not elm.cavity:
values.append(mean([fesom_data[season,elm.nodes[0].id], fesom_data[season,elm.nodes[1].id], fesom_data[season,elm.nodes[2].id]]))
# Plot FESOM data
ax = fig.add_subplot(2, 4, season+5, aspect='equal')
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values))
img.set_clim(vmin=0, vmax=1)
img.set_edgecolor('face')
ax.add_collection(img)
xlim([bdry1, bdry2])
ylim([bdry3, bdry4])
axis('off')
if season == 0:
text(-39, 0, 'FESOM', fontsize=24, ha='right')
# Add a horizontal colorbar at the bottom
cbaxes = fig.add_axes([0.25, 0.04, 0.5, 0.02])
cbar = colorbar(img, orientation='horizontal', ticks=arange(0,1+0.25,0.25), cax=cbaxes)
cbar.ax.tick_params(labelsize=16)
# Add the main title
suptitle('Sea ice concentration', fontsize=30)
# Decrease space between plots
subplots_adjust(wspace=0.025,hspace=0.025)
# Finished
if save:
fig.savefig(fig_name)
else:
fig.show()
chrom_set.add(segSeqD[i[0]][0])
# for i in range(1,24):
# chrom_set.add("chr" + str(i))
sorted_chrom = sorted(chrom_set,key=lambda x:int(x.rsplit("chr")[-1]))
sorted_chrom_colors = [chromosome_colors[x] for x in sorted_chrom]
legend_patches = []
for chrom,color in zip(sorted_chrom,sorted_chrom_colors):
legend_patches.append(mpatches.Patch(color=color,label=chrom))
plt.legend(handles=legend_patches,fontsize=8,loc=3,bbox_to_anchor=(-.3,.15))
p = PatchCollection(patches)
p.set_facecolor(f_color_v)
p.set_edgecolor(e_color_v)
p.set_linewidth(lw_v)
ax.add_collection(p)
ax.set_aspect(1.0)
plt.axis('off')
plt.savefig(fname + '.png',dpi=600)
plt.savefig(fname + '.pdf',format='pdf')
plt.close()
print "done plotting bionano"
#handles basic (non-bionano case)
if args.feature_labels:
#make a separate figure
plt.clf()
fig, ax = plt.subplots()
class ArrayDisplay:
"""
Display a top-town view of a telescope array
"""
def __init__(self, telx, tely, tel_type=None, radius=20,
axes=None, title="Array", autoupdate=True):
if tel_type is None:
tel_type = np.ones(len(telx))
patches = [Rectangle(xy=(x-radius/2, y-radius/2), width=radius, height=radius, fill=False)
for x, y in zip(telx, tely)]
self.autoupdate = autoupdate
self.telescopes = PatchCollection(patches, match_original=True)
self.telescopes.set_clim(1, 9)
rgb = matplotlib.cm.Set1((tel_type-1)/9)
self.telescopes.set_edgecolor(rgb)
self.telescopes.set_linewidth(2.0)
self.axes = axes if axes is not None else plt.gca()
self.axes.add_collection(self.telescopes)
self.axes.set_aspect(1.0)
self.axes.set_title(title)
self.axes.set_xlim(-1000, 1000)
self.axes.set_ylim(-1000, 1000)
self.axes_hillas = axes if axes is not None else plt.gca()
@property
def values(self):
"""An array containing a value per telescope"""
return self.telescopes.get_array()
@values.setter
def values(self, values):
""" set the telescope colors to display """
self.telescopes.set_array(values)
self._update()
def _update(self):
""" signal a redraw if necessary """
if self.autoupdate:
plt.draw()
def add_ellipse(self, centroid, length, width, angle, **kwargs):
"""
plot an ellipse on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
length: float
major axis
width: float
minor axis
angle: float
rotation angle wrt x-axis about the centroid, anticlockwise, in radians
asymmetry: float
3rd-order moment for directionality if known
kwargs:
any MatPlotLib style arguments to pass to the Ellipse patch
"""
ellipse = Ellipse(xy=centroid, width=length, height=width,
angle=np.degrees(angle), fill=True, **kwargs)
self.axes.add_patch(ellipse)
return ellipse
def add_polygon(self, centroid, radius, nsides=3, **kwargs):
"""
plot a polygon on top of the camera
Parameters
----------
centroid: (float, float)
position of centroid
radius: float
radius
nsides: int
Number of points on polygon
kwargs:
any MatPlotLib style arguments to pass to the RegularPolygon patch
"""
polygon = RegularPolygon(xy=centroid, radius=radius, numVertices=nsides, **kwargs)
self.axes.add_patch(polygon)
return polygon
def overlay_moments(self, momparams, tel_position, scale_fac, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
tel_position: list
(x, y) positions of each telescope
scale_fac: float
scaling factor to apply to width and length when overlaying moments
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
ellipse_list = list()
size_list = list()
i = 0
for h in momparams:
length = u.Quantity(momparams[h].length).value * scale_fac
width = u.Quantity(momparams[h].width).value * scale_fac
size_list.append(u.Quantity(momparams[h].size).value)
tel_x = u.Quantity(tel_position[0][i]).value
tel_y = u.Quantity(tel_position[1][i]).value
i += 1
ellipse = Ellipse(xy=(tel_x,tel_y), width=length, height=width,
angle=np.degrees(momparams[h].psi.rad))
ellipse_list.append(ellipse)
patches = PatchCollection(ellipse_list, **kwargs)
patches.set_clim(0, 1000) # Set ellipse colour based on image size
patches.set_array(np.asarray(size_list))
self.axes_hillas.add_collection(patches)
def overlay_axis(self, momparams, tel_position, **kwargs):
"""helper to overlay ellipse from a `reco.MomentParameters` structure
Parameters
----------
momparams: `reco.MomentParameters`
structuring containing Hillas-style parameterization
tel_position: list
(x, y) positions of each telescope
kwargs: key=value
any style keywords to pass to matplotlib (e.g. color='red'
or linewidth=6)
"""
# strip off any units
i = 0
for h in momparams:
tel_x = u.Quantity(tel_position[0][i]).value
tel_y = u.Quantity(tel_position[1][i]).value
psi = u.Quantity(momparams[h].psi).value
x_sc = [tel_x - np.cos(psi) * 10000, tel_x + np.cos(psi) * 10000]
y_sc = [tel_y - np.sin(psi) * 10000, tel_y + np.sin(psi) * 10000]
i += 1
self.axes_hillas.add_line(Line2D(x_sc, y_sc, linestyle='dashed', color='black'))
colors = []
if dimension == 1:
for i in range(hcuts):
patches += [ Wedge(origin, radius, 360.0/2/2/(hcuts-1)*(2*i-1), 360.0/2/2/(hcuts-1)*(2*i+1), width=width) ]
#TODO
colors += [i%5]
elif dimension == 2:
for i in range(hcuts):
patches += [ Wedge(origin, radius, 360.0/hcuts*i, 360.0/hcuts*(i+1), width=width) ]
#TODO
colors += [i%5]
else:
#TODO
pass
collection = PatchCollection(patches, cmap = cm.jet)
collection.set_array(np.array(colors))
collection.set_edgecolor('none')
fig, ax = plt.subplots()
ax.add_collection(collection)
plt.colorbar(collection)
plt.xlim(-1,1)
plt.ylim(-1,1)
plt.show()
file.close()
def plot_wct (mesh_path, fig_name):
# Plotting parameters
circumpolar = True
lat_max = -30 + 90
font_sizes = [240, 192, 160]
# Build triangular patches for each element
elements, patches = make_patches(mesh_path, circumpolar)
# Read the ice shelf draft for each node
file = open(mesh_path + 'shelf.out', 'r')
node_shelf = []
for line in file:
node_shelf.append(-float(line))
file.close()
# Read the depth for each node
file = open(mesh_path + 'depth.out', 'r')
node_depth = []
for line in file:
node_depth.append(-float(line))
file.close()
# Calculate water column thickness
node_wct = abs(array(node_depth)) - abs(array(node_shelf))
# For each element, calculate the minimum wct of the 3 component nodes
elm_wct = []
for elm in elements:
wct1 = node_wct[(elm.nodes[0]).id-1]
wct2 = node_wct[(elm.nodes[1]).id-1]
wct3 = node_wct[(elm.nodes[2]).id-1]
elm_wct.append(amin(array([wct1, wct2, wct3])))
# Set up figure
fig = figure(figsize=(128, 96))
ax = fig.add_subplot(1,1,1, aspect='equal')
# Set colours for patches and add them to plot
img = PatchCollection(patches, cmap=jet)
img.set_array(array(elm_wct))
img.set_edgecolor('face')
ax.add_collection(img)
# Configure plot
xlim([-lat_max, lat_max])
ylim([-lat_max, lat_max])
ax.get_xaxis().set_ticks([])
ax.get_yaxis().set_ticks([])
title('Water column thickness (m)', fontsize=font_sizes[0])
cbar = colorbar(img)
cbar.ax.tick_params(labelsize=font_sizes[2])
axis('off')
# Plot specified points
#problem_ids = [291, 297]
#problem_x = []
#problem_y = []
#for elm in elements:
#for i in range(3):
#if elm.nodes[i].id in problem_ids:
#problem_x.append(elm.x[i])
#problem_y.append(elm.y[i])
#problem_ids.remove(elm.nodes[i].id)
#ax.plot(problem_x, problem_y, 'or')
savefig(fig_name)
def patchMatrix(A, xmap=None, ymap=None, ax=None, cMin=None, cMax=None,
logScale=None, label=None, dx=1, **kwargs):
""" plot previously generated (generateVecMatrix) matrix
Parameters
----------
A : numpy.array2d
matrix to show
xmap : dict {i:num}
dict (must match A.shape[0])
ymap : iterable
vector for x axis (must match A.shape[0])
ax : mpl.axis
axis to plot, if not given a new figure is created
cMin/cMax : float
minimum/maximum color values
logScale : bool
logarithmic colour scale [min(A)>0]
label : string
colorbar label
"""
mat = np.ma.masked_where(A == 0.0, A, False)
if cMin is None:
cMin = np.min(mat)
if cMax is None:
cMax = np.max(mat)
if logScale is None:
logScale = (cMin > 0.0)
if logScale:
norm = LogNorm(vmin=cMin, vmax=cMax)
else:
norm = Normalize(vmin=cMin, vmax=cMax)
if 'ax' is None:
fig, ax = plt.subplots()
iy, ix = np.nonzero(A) # != 0)
recs = []
vals = []
for i in range(len(ix)):
recs.append(Rectangle((ix[i]-dx/2, iy[i]-0.5), dx, 1))
vals.append(A[iy[i], ix[i]])
pp = PatchCollection(recs)
col = ax.add_collection(pp)
pp.set_edgecolor(None)
pp.set_linewidths(0.0)
if 'cmap' in kwargs:
pp.set_cmap(kwargs.pop('cmap'))
pp.set_norm(norm)
pp.set_array(np.array(vals))
pp.set_clim(cMin, cMax)
xval = [k for k in xmap.keys()]
ax.set_xlim(min(xval)-dx/2, max(xval)+dx/2)
ax.set_ylim(len(ymap)+0.5, -0.5)
updateAxes_(ax)
cbar = None
if kwargs.pop('colorBar', True):
cbar = pg.mplviewer.createColorbar(col, cMin=cMin, cMax=cMax, nLevs=5,
label=label)
return ax, cbar
y = pontos[:,1][t_i][0]
# a cor do polígono vem da imagem original
cores.append(image_rgb[y-1][x-1])
# criamos um polígono para representar o triângulo (aqui começa o desenho
# que estamos sintetizando)
poly = Polygon(pontos[:,[0,1]][t_i], True)
patches.append(poly)
# adicionamos os polígonos a uma coleção de colagens
p = PatchCollection(patches)
#print 'patches:', len(patches), 'regions:', len(cores)
# definimos as cores conforme imagem original e as bordas em transparente
p.set_facecolor(cores)
p.set_edgecolor(cores)
p.set_alpha(.8)
# plotamos o desenho sintetizado
ax3.add_collection(p)
for ax in axes:
ax.axis('off')
nome_pintura = '%s_passos.svg' % IMG[:-4]
fig.savefig(nome_pintura)
print 'pintura gerada em: %s' % nome_pintura
# lista com coordenadas de cada triângulo
# print 'coords. triângulos:', coords_tri
# número de triângulos
# print 'num. de triângulos:', len(coords_tri)
def aice_hi_seasonal(mesh_path, file_path1, file_path2, save=False, fig_name=None):
# FESOM parameters
circumpolar = True
mask_cavities = True
# Season names for plot titles
season_names = ["DJF", "MAM", "JJA", "SON"]
# Build FESOM mesh
elements, patches = make_patches(mesh_path, circumpolar, mask_cavities)
# Get seasonal averages of the FESOM output
# This is hard-coded and ugly
id = Dataset(file_path1, "r")
n2d = id.variables["area"].shape[1]
aice = zeros([4, n2d])
hi = zeros([4, n2d])
# DJF: 1/5 of index 67 (1-based) and indices 68-73 in file1; indices 1-11
# and 4/5 of index 12 in file2; 90 days in total
aice[0, :] = id.variables["area"][66, :] + sum(id.variables["area"][67:73, :] * 5, axis=0)
hi[0, :] = id.variables["hice"][66, :] + sum(id.variables["hice"][67:73, :] * 5, axis=0)
id.close()
id = Dataset(file_path2, "r")
aice[0, :] += sum(id.variables["area"][0:11, :] * 5, axis=0) + id.variables["area"][11, :] * 4
aice[0, :] /= 90
hi[0, :] += sum(id.variables["hice"][0:11, :] * 5, axis=0) + id.variables["hice"][11, :] * 4
hi[0, :] /= 90
# MAM: 1/5 of index 12, indices 13-30, and 1/5 of index 31 in file2;
# 92 days in total
aice[1, :] = (
id.variables["area"][11, :] + sum(id.variables["area"][12:30, :] * 5, axis=0) + id.variables["area"][30, :]
)
aice[1, :] /= 92
hi[1, :] = (
id.variables["hice"][11, :] + sum(id.variables["hice"][12:30, :] * 5, axis=0) + id.variables["hice"][30, :]
)
hi[1, :] /= 92
# JJA: 4/5 of index 31, indices 32-48, and 3/5 of index 49 in file2;
# 92 days in total
aice[2, :] = (
id.variables["area"][30, :] * 4
+ sum(id.variables["area"][31:48, :] * 5, axis=0)
+ id.variables["area"][48, :] * 3
)
aice[2, :] /= 92
hi[2, :] = (
id.variables["hice"][30, :] * 4
+ sum(id.variables["hice"][31:48, :] * 5, axis=0)
+ id.variables["hice"][48, :] * 3
)
hi[2, :] /= 92
# SON: 2/5 of index 49, indices 50-66, and 4/5 of index 67 in file2;
# 91 days in total
aice[3, :] = (
id.variables["area"][48, :] * 2
+ sum(id.variables["area"][49:66, :] * 5, axis=0)
+ id.variables["area"][66, :] * 4
)
aice[3, :] /= 91
hi[3, :] = (
id.variables["hice"][48, :] * 2
+ sum(id.variables["hice"][49:66, :] * 5, axis=0)
+ id.variables["hice"][66, :] * 4
)
hi[3, :] /= 91
id.close()
# Plot
fig = figure(figsize=(20, 9))
# Loop over seasons
for season in range(4):
# aice
# Build an array of FESOM data values corresponding to each Element
values1 = []
for elm in elements:
# For each element not in an ice shelf cavity, append the mean
# value for the 3 component Nodes
if not elm.cavity:
values1.append(
mean([aice[season, elm.nodes[0].id], aice[season, elm.nodes[1].id], aice[season, elm.nodes[2].id]])
)
ax = fig.add_subplot(2, 4, season + 1, aspect="equal")
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values1))
img.set_clim(vmin=0, vmax=1)
img.set_edgecolor("face")
ax.add_collection(img)
xlim([-35, 35])
ylim([-33, 37])
axis("off")
if season == 0:
text(-39, 0, "aice (%)", fontsize=21, ha="right")
title(season_names[season], fontsize=24)
if season == 3:
cbaxes1 = fig.add_axes([0.92, 0.55, 0.01, 0.3])
cbar1 = colorbar(img, ticks=arange(0, 1 + 0.25, 0.25), cax=cbaxes1)
cbar1.ax.tick_params(labelsize=16)
# hi
values2 = []
for elm in elements:
# For each element not in an ice shelf cavity, append the mean
# value for the 3 component Nodes
if not elm.cavity:
values2.append(
mean([hi[season, elm.nodes[0].id], hi[season, elm.nodes[1].id], hi[season, elm.nodes[2].id]])
)
ax = fig.add_subplot(2, 4, season + 5, aspect="equal")
img = PatchCollection(patches, cmap=jet)
img.set_array(array(values2))
img.set_clim(vmin=0, vmax=1.5)
img.set_edgecolor("face")
ax.add_collection(img)
xlim([-35, 35])
ylim([-33, 37])
axis("off")
if season == 0:
text(-39, 0, "hi (m)", fontsize=21, ha="right")
if season == 3:
cbaxes2 = fig.add_axes([0.92, 0.15, 0.01, 0.3])
cbar2 = colorbar(img, ticks=arange(0, 1.5 + 0.5, 0.5), cax=cbaxes2)
cbar2.ax.tick_params(labelsize=16)
# Decrease space between plots
subplots_adjust(wspace=0.025, hspace=0.025)
# Finished
if save:
fig.savefig(fig_name)
else:
fig.show()
