def plot_temperature_biases():
seasons = ["(a) Annual", " (b) Winter (DJF)", "(c) Spring (MAM)", "(d) Summer (JJA)", "(e) Fall (SON)"]
months = [range(1, 13), [12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11] ]
season_to_months = dict(zip(seasons, months))
cru_var_name = "tmp"
cru_data_store, crcm4_data_store = _get_comparison_data(cru_var_name= cru_var_name,
season_to_months=season_to_months)
x, y = polar_stereographic.xs, polar_stereographic.ys
i_array, j_array = _get_routing_indices()
x_min, x_max, y_min, y_max = plot_utils.get_ranges(x[i_array, j_array], y[i_array, j_array])
plot_utils.apply_plot_params(width_pt= None, font_size=9, aspect_ratio=2.5)
fig = plt.figure()
assert isinstance(fig, Figure)
basemap = polar_stereographic.basemap
assert isinstance(basemap, Basemap)
gs = gridspec.GridSpec(3,2)
color_map = my_cm.get_red_blue_colormap(ncolors = 14, reversed=True)
clevels = xrange(-8, 9, 2)
all_plot_axes = []
for i, season in enumerate(seasons):
if not i:
ax = fig.add_subplot(gs[0,:])
else:
row, col = (i - 1) // 2 + 1, (i - 1) % 2
ax = fig.add_subplot(gs[row, col])
all_plot_axes.append(ax)
assert isinstance(ax, Axes)
delta = crcm4_data_store[season] - cru_data_store[season]
if cru_var_name == "tmp": delta -= 273.15
#delta = maskoceans(polar_stereographic.lons, polar_stereographic.lats, delta)
save = delta[i_array, j_array]
delta[:, :] = np.ma.masked
delta[i_array, j_array] = save
img = basemap.pcolormesh(x, y, delta, cmap = color_map, vmin = -7, vmax = 7)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "8%", pad="3%")
int_ticker = LinearLocator(numticks = color_map.N + 1)
fig.colorbar(img, cax = cax, ticks = MultipleLocator(base = 2))
ax.set_title(season)
for the_ax in all_plot_axes:
the_ax.set_xlim(x_min, x_max)
the_ax.set_ylim(y_min, y_max)
basemap.drawcoastlines(ax = the_ax, linewidth = 0.1)
plot_utils.draw_meridians_and_parallels(basemap, step_degrees=30.0, ax = the_ax)
plot_basin_boundaries_from_shape(basemap, axes = the_ax, linewidth=0.4)
put_selected_stations(the_ax, basemap, i_array, j_array)
#gs.tight_layout(fig)
fig.suptitle("T(2m), degrees, CRCM4 - CRU")
fig.savefig("seasonal_{0}_ccc.png".format(cru_var_name))
def plot_data(data, i_array, j_array, name='AEX', title = None, digits = 1,
color_map = mpl.cm.get_cmap('RdBu'),
minmax = (None, None),
units = '%',
colorbar_orientation = 'vertical'
):
if name != None:
plt.figure()
to_plot = np.ma.masked_all(xs.shape)
for index, i, j in zip( range(len(data)), i_array, j_array):
to_plot[i, j] = data[index]
print np.ma.min(data), np.ma.max(data)
# m.pcolor(xs, ys, to_plot, cmap = mpl.cm.get_cmap('RdBu_r'))
extent = [np.min(xs), np.max(xs), np.min(ys), np.max(ys)]
plt.imshow(to_plot.transpose().copy(), interpolation = 'nearest' ,
extent = extent,
origin = 'lower',
cmap = color_map,
vmin = minmax[0],
vmax = minmax[1]
)
plot_basin_boundaries_from_shape(m, linewidth = 1)
m.drawrivers()
m.drawcoastlines()
draw_meridians_and_parallels(m, step_degrees = 30)
int_ticker = LinearLocator()
cb = plt.colorbar(ticks = int_ticker, orientation = colorbar_orientation)
cb.ax.set_ylabel(units)
override = {'fontsize': 20,
'verticalalignment': 'baseline',
'horizontalalignment': 'center'}
plt.title(title if title != None else name, override)
ymin, ymax = plt.ylim()
plt.ylim(ymin + 0.12 * (ymax - ymin), ymax * 0.32)
xmin, xmax = plt.xlim()
plt.xlim(xmin + (xmax - xmin) * 0.65, 0.85*xmax)
if name != None:
plt.savefig(name + '.png', bbox_inches = 'tight')
def plot_forcing_error(errors_map, i_list, j_list):
pylab.rcParams.update(params)
to_plot = ma.masked_all(xs.shape)
err = None
for id, error in errors_map.iteritems():
if err == None:
err = np.zeros(error.shape)
err += error
err = err / len(errors_map)
for i, j, er in zip(i_list, j_list, err):
to_plot[i,j] = er
n_levels = 20
plt.imshow(to_plot.transpose().copy(), extent = [np.min(xs), np.max(xs), ys.min(), ys.max()],
origin = 'lower',
interpolation = 'bilinear' ,
cmap = mpl.cm.get_cmap('RdBu_r', n_levels),
vmin = -30, vmax = 30)
int_ticker = MaxNLocator(nbins=n_levels, integer=True)
cb = plt.colorbar(ticks = int_ticker)
cb.ax.set_ylabel('%')
basemap.drawcoastlines()
print np.min(lons), np.max(lons), lats.min(), lats.max()
plot_basin_boundaries_from_shape(basemap, linewidth = 0.5)
ymin, ymax = plt.ylim()
plt.ylim(ymin + 0.12 * (ymax - ymin), ymax * 0.32)
xmin, xmax = plt.xlim()
plt.xlim(xmin + (xmax - xmin) * 0.65, 0.85*xmax)
draw_meridians_and_parallels(basemap, 10)
plt.savefig("forcing_errors.png")
def test_interpolation():
lons, lats, interpolated = get_topography_interpolated_to_graham_grid(
grid_lower_left = GeoPoint(-85.0, 40.0),
grid_shape = (380, 280)
)
print lons.shape
print lats.shape
print interpolated.shape
print 'done interpolating'
m = Basemap(llcrnrlon=-85, llcrnrlat=40,
urcrnrlon=-53, urcrnrlat=65)
lons, lats = m(lons, lats)
m.contourf(lons, lats, interpolated)
m.drawcoastlines()
draw_meridians_and_parallels(m)
plt.colorbar()
plt.savefig('interpolated_topo.png')
pass
def default_basemap_scatter():
# m = Basemap()
# m.drawcoastlines(linewidth = 0.5)
# longitudes_array = np.arange(-85, -55, 5.0 / MINUTES_PER_DEGREE)
# latitudes_array = np.arange(70, 40, - 5.0 / MINUTES_PER_DEGREE)
# X, Y = pylab.meshgrid(longitudes_array, latitudes_array)
# longitudes_array, latitudes_array = m(X, Y)
# m.contourf(longitudes_array, latitudes_array, longitudes_array)
# plt.figure()
# m = Basemap(boundinglat = 40, projection = 'npstere', lat_0=60, lon_0=0)
# m.drawcoastlines()
# draw_meridians_and_parallels(m, 25)
plt.figure()
m = Basemap(projection = 'npstere',
lat_ts = 60, lat_0 = 60, lon_0 = -115, boundinglat = 40, resolution='i')
m.drawcoastlines()
m.drawrivers(color = 'blue', linewidth = 1)
dx = 45000.0
[px, py] = m(-61.5*dx, -179.8*dx)
m.scatter(px, py , c="red")
draw_meridians_and_parallels(m, 25)
[ymin, ymax] = plt.ylim()
plt.ylim(ymin + 0.12 * (ymax - ymin), ymax * 0.32)
[xmin, xmax] = plt.xlim()
plt.xlim(xmin + (xmax - xmin) * 0.65, 0.85*xmax)
plt.show()
def plot_temp():
seasons = ["(a) Annual", " (b) Winter (DJF)", "(c) Spring (MAM)", "(d) Summer (JJA)", "(e) Fall (SON)"]
months = [range(1, 13), [12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11] ]
season_to_months = dict(zip(seasons, months))
#take out annual
seasons.pop(0)
#put new numbering for the subplots
new_numbering = ["a", "b", "c", "d"]
var_name = "st"
year_range_c = xrange(1970,2000)
year_range_f = xrange(2041,2071)
x, y = polar_stereographic.xs, polar_stereographic.ys
i_array, j_array = _get_routing_indices()
x_min, x_max, y_min, y_max = plot_utils.get_ranges(x[i_array, j_array], y[i_array, j_array])
plot_utils.apply_plot_params(width_pt= None, font_size=9, aspect_ratio=2.5)
fig = plt.figure()
assert isinstance(fig, Figure)
basemap = polar_stereographic.basemap
assert isinstance(basemap, Basemap)
gs = gridspec.GridSpec(3,2)
#color_map = mpl.cm.get_cmap(name="jet", lut=10)
clevels = xrange(-8, 9, 2)
all_plot_axes = []
#determine min an max for color scales
min_val = np.inf
max_val = -np.inf
for season in seasons:
current = _get_data(v_name=var_name, months = season_to_months[season],
member_list=members.current_ids, year_range=year_range_c)
future = _get_data(v_name=var_name, months = season_to_months[season],
member_list=members.future_ids, year_range=year_range_f)
current_m = np.mean(current, axis=0)
future_m = np.mean(future, axis= 0)
delta = future_m[i_array, j_array] - current_m[i_array, j_array]
the_min = delta.min()
the_max = delta.max()
min_val = min(min_val, the_min)
max_val = max(max_val, the_max)
min_val = np.floor(min_val)
max_val = np.ceil(max_val)
color_map = my_cm.get_red_blue_colormap(ncolors = 10, reversed=True)
if min_val >= 0:
color_map = my_cm.get_red_colormap(ncolors=10)
for i, season in enumerate(seasons):
row, col = i // 2, i % 2
ax = fig.add_subplot(gs[row, col ])
all_plot_axes.append(ax)
current = _get_data(v_name=var_name, months = season_to_months[season],
member_list=members.current_ids, year_range=year_range_c)
future = _get_data(v_name=var_name, months = season_to_months[season],
member_list=members.future_ids, year_range=year_range_f)
# t, p = stats.ttest_ind(current, future, axis=0)
# significant = np.array(p <= 0.05)
significant = calculate_significance_using_bootstrap(current, future)
assert not np.all(~significant)
assert not np.all(significant)
current_m = np.mean(current, axis=0)
future_m = np.mean(future, axis= 0)
delta = (future_m - current_m)
assert isinstance(ax, Axes)
#delta = maskoceans(polar_stereographic.lons, polar_stereographic.lats, delta)
save = delta[i_array, j_array]
delta = np.ma.masked_all(delta.shape)
delta[i_array, j_array] = save
d_min = np.floor( np.min(save) )
d_max = np.ceil( np.max(save) )
img = basemap.pcolormesh(x, y, delta, cmap = color_map, vmin = min_val, vmax = max_val)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "8%", pad="3%")
assert isinstance(cax, Axes)
int_ticker = LinearLocator(numticks = color_map.N + 1)
cb = fig.colorbar(img, cax = cax, ticks = int_ticker)
cax.set_title("$^{\\circ}{\\rm C}$")
where_significant = significant
significant = np.ma.masked_all(significant.shape)
significant[~where_significant] = 0
basemap.pcolormesh( x, y, significant , cmap = mpl.cm.get_cmap(name = "gray", lut = 3),
vmin = -1, vmax = 1, ax = ax)
ax.set_title( season.replace( re.findall( "([a-z])", season)[0], new_numbering[i]) )
for the_ax in all_plot_axes:
the_ax.set_xlim(x_min, x_max)
the_ax.set_ylim(y_min, y_max)
basemap.drawcoastlines(ax = the_ax, linewidth = 0.1)
plot_utils.draw_meridians_and_parallels(basemap, step_degrees=30.0, ax = the_ax)
plot_basin_boundaries_from_shape(basemap, axes = the_ax, linewidth=0.4)
#gs.update(wspace=0.5)
fig.tight_layout()
#fig.suptitle("Projected changes, T(2m), degrees, CRCM4")
fig.savefig("proj_change_{0}_ccc.png".format(var_name))
def plot_swe():
seasons = ["(a) Annual", " (b) Winter (DJF)", "(c) Spring (MAM)", "(d) Summer (JJA)", "(e) Fall (SON)"]
months = [range(1, 13), [12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11] ]
season_to_months = dict(zip(seasons, months))
var_name = "sno"
year_range_c = xrange(1970,2000)
year_range_f = xrange(2041,2071)
x, y = polar_stereographic.xs, polar_stereographic.ys
i_array, j_array = _get_routing_indices()
x_min, x_max, y_min, y_max = plot_utils.get_ranges(x[i_array, j_array], y[i_array, j_array])
_generate_mask_of_domain_of_interest(i_array, j_array)
if True: return
plot_utils.apply_plot_params(width_pt= None, font_size=9, aspect_ratio=2.5)
fig = plt.figure()
assert isinstance(fig, Figure)
basemap = polar_stereographic.basemap
assert isinstance(basemap, Basemap)
gs = gridspec.GridSpec(3,4, height_ratios=[1,1,1], width_ratios=[1,1,1,1])
#color_map = my_cm.get_
color_map = my_cm.get_red_blue_colormap(ncolors = 10, reversed=True)
#color_map = mpl.cm.get_cmap(name="jet_r", lut=10)
clevels = xrange(-8, 9, 2)
all_plot_axes = []
for i, season in enumerate(seasons):
if not i:
ax = fig.add_subplot(gs[0,1:3])
else:
row, col = (i - 1) // 2 + 1, (i - 1) % 2
ax = fig.add_subplot(gs[row, col * 2 : col * 2 + 2 ])
all_plot_axes.append(ax)
current = _get_data(v_name=var_name, months = season_to_months[season],
member_list=members.current_ids, year_range=year_range_c)
future = _get_data(v_name=var_name, months = season_to_months[season],
member_list=members.future_ids, year_range=year_range_f)
t, p = stats.ttest_ind(current, future, axis=0)
#TODO: change it back to p <= 0.05 wheen doing real sign test
significant = np.array(p <= 1)
assert not np.all(~significant)
assert not np.all(significant)
current_m = np.mean(current, axis=0)
future_m = np.mean(future, axis= 0)
delta = (future_m - current_m)
delta = np.array(delta)
assert isinstance(ax, Axes)
#delta = maskoceans(polar_stereographic.lons, polar_stereographic.lats, delta)
save = delta[i_array, j_array]
delta = np.ma.masked_all(delta.shape)
delta[i_array, j_array] = save
d_min = np.floor( np.min(save) )
d_max = np.ceil( np.max(save) )
bounds = plot_utils.get_boundaries_for_colobar(d_min, d_max, color_map.N, lambda x: np.round(x, decimals=10))
print bounds
bn = BoundaryNorm(bounds, color_map.N)
d = np.max( np.abs([d_min, d_max]) )
print season, np.min(delta), np.max(delta)
#delta = np.ma.masked_where(delta < 0, delta )
img = basemap.pcolormesh(x, y, delta, cmap = color_map, norm = bn, vmin = bounds[0], vmax = bounds[-1])
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "8%", pad="3%")
assert isinstance(cax, Axes)
int_ticker = LinearLocator(numticks = color_map.N + 1)
cb = fig.colorbar(img, cax = cax, ticks = bounds, boundaries = bounds)
where_significant = significant
significant = np.ma.masked_all(significant.shape)
significant[(~where_significant)] = 0
save = significant[i_array, j_array]
significant = np.ma.masked_all(significant.shape)
significant[i_array, j_array] = save
basemap.pcolormesh( x, y, significant , cmap = mpl.cm.get_cmap(name = "gray", lut = 3),
vmin = -1, vmax = 1, ax = ax)
ax.set_title(season)
for the_ax in all_plot_axes:
the_ax.set_xlim(x_min, x_max)
the_ax.set_ylim(y_min, y_max)
basemap.drawcoastlines(ax = the_ax, linewidth = 0.1)
plot_utils.draw_meridians_and_parallels(basemap, step_degrees=30.0, ax = the_ax)
plot_basin_boundaries_from_shape(basemap, axes = the_ax, linewidth=0.4)
gs.update(wspace=0.5)
#gs.tight_layout(fig)
fig.suptitle("Projected changes, SWE, mm, CRCM4")
fig.savefig("proj_change_{0}_ccc.png".format(var_name))
pass
def plot_precip(data_path = "/home/huziy/skynet1_rech3/crcm4_data"):
seasons = ["(a) Annual", " (b) Winter (DJF)", "(c) Spring (MAM)", "(d) Summer (JJA)", "(e) Fall (SON)"]
months = [range(1, 13), [12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11] ]
season_to_months = dict(zip(seasons, months))
#remove annual
seasons.pop(0)
#put new numbering for the subplots
new_numbering = ["a", "b", "c", "d"]
var_name = "pcp"
year_range_c = xrange(1970,2000)
year_range_f = xrange(2041,2071)
x, y = polar_stereographic.xs, polar_stereographic.ys
i_array, j_array = _get_routing_indices()
x_min, x_max, y_min, y_max = plot_utils.get_ranges(x[i_array, j_array], y[i_array, j_array])
plot_utils.apply_plot_params(width_pt= None, font_size=9, aspect_ratio=2.5)
fig = plt.figure()
assert isinstance(fig, Figure)
basemap = polar_stereographic.basemap
assert isinstance(basemap, Basemap)
gs = gridspec.GridSpec(3,2, height_ratios=[1,1,1], width_ratios=[1,1])
#determine min an max for color scales
min_val = np.inf
max_val = -np.inf
for season in seasons:
current = _get_data(v_name=var_name, months = season_to_months[season],
member_list=members.current_ids, year_range=year_range_c)
future = _get_data(v_name=var_name, months = season_to_months[season],
member_list=members.future_ids, year_range=year_range_f)
current_m = np.mean(current, axis=0)
future_m = np.mean(future, axis= 0)
delta = future_m[i_array, j_array] - current_m[i_array, j_array]
the_min = delta.min()
the_max = delta.max()
min_val = min(min_val, the_min)
max_val = max(max_val, the_max)
min_val = np.floor(min_val)
max_val = np.ceil(max_val)
color_map = my_cm.get_red_blue_colormap(ncolors = 10, reversed=False)
#color_map = mpl.cm.get_cmap(name="jet_r", lut=10)
clevels = xrange(-8, 9, 2)
all_plot_axes = []
for i, season in enumerate(seasons):
row, col = i // 2, i % 2
ax = fig.add_subplot(gs[row, col ])
all_plot_axes.append(ax)
current = _get_data(data_folder=data_path, v_name=var_name, months = season_to_months[season],
member_list=members.current_ids, year_range=year_range_c)
future = _get_data(data_folder=data_path, v_name=var_name, months = season_to_months[season],
member_list=members.future_ids, year_range=year_range_f)
#t, p = stats.ttest_ind(current, future, axis=0)
#significant = np.array(p <= 0.05)
significant = calculate_significance_using_bootstrap(current, future)
assert not np.all(~significant)
assert not np.all(significant)
current_m = np.mean(current, axis=0)
future_m = np.mean(future, axis= 0)
seconds_per_day = 24 * 60 * 60
delta = (future_m - current_m) * seconds_per_day
delta = np.array(delta)
assert isinstance(ax, Axes)
#delta = maskoceans(polar_stereographic.lons, polar_stereographic.lats, delta)
save = delta[i_array, j_array]
delta = np.ma.masked_all(delta.shape)
delta[i_array, j_array] = save
d_min = np.floor( min_val * 10 ) / 10.0
d_max = np.ceil( max_val *10 ) / 10.0
if d_min > 0: color_map = my_cm.get_blue_colormap(ncolors=10)
img = basemap.pcolormesh(x, y, delta, cmap = color_map, vmin = d_min, vmax = d_max)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "8%", pad="3%")
assert isinstance(cax, Axes)
int_ticker = LinearLocator(numticks = color_map.N + 1)
cb = fig.colorbar(img, cax = cax, ticks = int_ticker)
cax.set_title("mm/d")
where_significant = significant
significant = np.ma.masked_all(significant.shape)
significant[(~where_significant)] = 0
save = significant[i_array, j_array]
significant = np.ma.masked_all(significant.shape)
significant[i_array, j_array] = save
basemap.pcolormesh( x, y, significant , cmap = mpl.cm.get_cmap(name = "gray", lut = 3),
vmin = -1, vmax = 1, ax = ax)
ax.set_title( season.replace( re.findall( "([a-z])", season)[0], new_numbering[i]) )
#plot djf swe change
season = " (b) Winter (DJF)"
ax = fig.add_subplot(gs[2, : ])
all_plot_axes.append(ax)
var_name = "sno"
current = _get_data(data_folder=data_path, v_name=var_name, months = season_to_months[season],
member_list=members.current_ids, year_range=year_range_c)
future = _get_data(data_folder=data_path, v_name=var_name, months = season_to_months[season],
member_list=members.future_ids, year_range=year_range_f)
#t, p = stats.ttest_ind(current, future, axis=0)
#significant = np.array(p <= 0.05)
significant = calculate_significance_using_bootstrap(current, future)
assert not np.all(~significant)
assert not np.all(significant)
current_m = np.mean(current, axis=0)
future_m = np.mean(future, axis= 0)
delta = future_m - current_m
delta = np.array(delta)
assert isinstance(ax, Axes)
#delta = maskoceans(polar_stereographic.lons, polar_stereographic.lats, delta)
save = delta[i_array, j_array]
delta = np.ma.masked_all(delta.shape)
delta[i_array, j_array] = save
d_min = np.floor( np.min(save) * 10 ) / 10.0
d_max = np.ceil( np.max(save) *10 ) / 10.0
if d_min >= 0: color_map = my_cm.get_blue_colormap(ncolors=10)
bounds = plot_utils.get_boundaries_for_colobar(d_min, d_max, color_map.N, lambda x: np.round(x, decimals=0))
bn = BoundaryNorm(bounds, color_map.N)
img = basemap.pcolormesh(x, y, delta, cmap = color_map, vmin = bounds[0], vmax = bounds[-1], norm = bn)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "8%", pad="3%")
assert isinstance(cax, Axes)
int_ticker = LinearLocator(numticks = color_map.N + 1)
cb = fig.colorbar(img, cax = cax, ticks = bounds)
cax.set_title("mm")
where_significant = significant
significant = np.ma.masked_all(significant.shape)
significant[(~where_significant)] = 0
save = significant[i_array, j_array]
significant = np.ma.masked_all(significant.shape)
significant[i_array, j_array] = save
basemap.pcolormesh( x, y, significant , cmap = mpl.cm.get_cmap(name = "gray", lut = 3),
vmin = -1, vmax = 1, ax = ax)
ax.set_title( season.replace( re.findall( "([a-z])", season)[0], "e"))
#finish swe djf
for the_ax in all_plot_axes:
the_ax.set_xlim(x_min, x_max)
the_ax.set_ylim(y_min, y_max)
basemap.drawcoastlines(ax = the_ax, linewidth = 0.1)
plot_utils.draw_meridians_and_parallels(basemap, step_degrees=30.0, ax = the_ax)
plot_basin_boundaries_from_shape(basemap, axes = the_ax, linewidth=0.4)
#gs.update(wspace=0.5)
#gs.tight_layout(fig)
#fig.suptitle("Projected changes, total precip (mm/day), CRCM4")
fig.tight_layout()
fig.savefig("proj_change_{0}_ccc.png".format(var_name))
pass
def plot_swe_biases():
seasons = ["(a) Annual", " (b) Winter (DJF)", "(c) Spring (MAM)", "(d) Summer (JJA)", "(e) Fall (SON)"]
months = [range(1, 13), [12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11] ]
season_to_months = dict(zip(seasons, months))
swe_obs_name = "swe"
cru_data_store, crcm4_data_store = _get_comparison_data_swe(swe_var_name= swe_obs_name,
season_to_months=season_to_months, start_date=datetime(1980,1,1), end_date=datetime(1996, 12, 31))
x, y = polar_stereographic.xs, polar_stereographic.ys
i_array, j_array = _get_routing_indices()
x_min, x_max, y_min, y_max = plot_utils.get_ranges(x[i_array, j_array], y[i_array, j_array])
plot_utils.apply_plot_params(width_pt= None, font_size=9, aspect_ratio=2.5)
fig = plt.figure()
assert isinstance(fig, Figure)
basemap = polar_stereographic.basemap
assert isinstance(basemap, Basemap)
gs = gridspec.GridSpec(3,2)
color_map = my_cm.get_red_blue_colormap(ncolors = 14, reversed=True)
clevels = xrange(-8, 9, 2)
all_plot_axes = []
for i, season in enumerate(seasons):
if not i:
ax = fig.add_subplot(gs[0,:])
else:
row, col = (i - 1) // 2 + 1, (i -1) % 2
ax = fig.add_subplot(gs[row, col])
all_plot_axes.append(ax)
assert isinstance(ax, Axes)
delta = crcm4_data_store[season] - cru_data_store[season]
#delta = maskoceans(polar_stereographic.lons, polar_stereographic.lats, delta)
save = delta[i_array, j_array]
delta = np.ma.masked_all(delta.shape)
delta[i_array, j_array] = save
vmax = np.ceil( np.max(save) / 10.0) * 10
vmin = np.floor( np.min(save) / 10.0) * 10
bounds = plot_utils.get_boundaries_for_colobar(vmin, vmax, color_map.N, lambda x: np.round(x, decimals = 1))
bn = BoundaryNorm(bounds, color_map.N)
img = basemap.pcolormesh(x, y, delta, cmap = color_map, vmin = vmin, vmax = vmax, norm = bn)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "8%", pad="3%")
int_ticker = LinearLocator(numticks = color_map.N + 1)
fig.colorbar(img, cax = cax, ticks = bounds, boundaries = bounds)
ax.set_title(season)
for the_ax in all_plot_axes:
the_ax.set_xlim(x_min, x_max)
the_ax.set_ylim(y_min, y_max)
basemap.drawcoastlines(ax = the_ax, linewidth = 0.1)
plot_utils.draw_meridians_and_parallels(basemap, step_degrees=30.0, ax = the_ax)
plot_basin_boundaries_from_shape(basemap, axes = the_ax, linewidth=0.4)
# put_selected_stations(the_ax, basemap, i_array, j_array)
#gs.tight_layout(fig)
fig.suptitle("SWE (mm), CRCM4 - Ross Brown dataset (1981-1997)")
fig.savefig("seasonal_{0}_ccc.png".format(swe_obs_name))
def plot_precip_biases():
seasons = ["(a) Annual", " (b) Winter (DJF)", "(c) Spring (MAM)", "(d) Summer (JJA)", "(e) Fall (SON)"]
months = [range(1, 13), [12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11] ]
season_to_months = dict(zip(seasons, months))
cru_var_name = "pre"
cru_data_store, crcm4_data_store = _get_comparison_data(
crcm4_data_folder="/home/huziy/skynet1_rech3/crcm4_data/aex_p1pcp",
cru_data_path = "data/cru_data/CRUTS3.1/cru_ts_3_10.1901.2009.pre.dat.nc",
cru_var_name=cru_var_name, season_to_months=season_to_months
)
x, y = polar_stereographic.xs, polar_stereographic.ys
i_array, j_array = _get_routing_indices()
x_min, x_max, y_min, y_max = plot_utils.get_ranges(x[i_array, j_array], y[i_array, j_array])
plot_utils.apply_plot_params(width_pt= None, font_size=9, aspect_ratio=2.5)
fig = plt.figure()
assert isinstance(fig, Figure)
basemap = polar_stereographic.basemap
assert isinstance(basemap, Basemap)
gs = gridspec.GridSpec(3,2, width_ratios=[1,1], height_ratios=[1, 1, 1])
color_map = my_cm.get_red_blue_colormap(ncolors = 16, reversed=False)
color_map.set_over("k")
color_map.set_under("k")
all_plot_axes = []
img = None
for i, season in enumerate(seasons):
if not i:
ax = fig.add_subplot(gs[0,:])
else:
row, col = (i - 1) // 2 + 1, (i - 1) % 2
ax = fig.add_subplot(gs[row, col])
all_plot_axes.append(ax)
assert isinstance(ax, Axes)
delta = crcm4_data_store[season] - cru_data_store[season]
save = delta[i_array, j_array]
delta[:, :] = np.ma.masked
delta[i_array, j_array] = save
img = basemap.pcolormesh(x, y, delta, cmap = color_map, vmin = -2, vmax = 2)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "8%", pad="3%")
int_ticker = LinearLocator(numticks = color_map.N + 1)
fig.colorbar(img, cax = cax, ticks = MultipleLocator(base = 0.5))
ax.set_title(season)
for the_ax in all_plot_axes:
assert isinstance(the_ax, Axes)
the_ax.set_xlim(x_min, x_max)
the_ax.set_ylim(y_min, y_max)
the_ax.set_xmargin(0)
the_ax.set_ymargin(0)
basemap.drawcoastlines(ax = the_ax, linewidth = 0.1)
plot_utils.draw_meridians_and_parallels(basemap, step_degrees=30.0, ax = the_ax)
plot_basin_boundaries_from_shape(basemap, axes = the_ax, linewidth=0.4)
# put_selected_stations(the_ax, basemap, i_array, j_array)
# ax = fig.add_subplot(gs[3,:])
# assert isinstance(ax, Axes)
# fig.colorbar(img, cax = ax, orientation = "horizontal", ticks = MultipleLocator(base = 0.5))
#gs.tight_layout(fig)
fig.suptitle("Total precip, mm/day, CRCM4 - CRU")
fig.savefig("seasonal_{0}_ccc.png".format(cru_var_name))
def plot_swe_and_temp_on_one_plot():
seasons = ["(a) Annual", " (b) Winter (DJF)", "(c) Spring (MAM)", "(d) Summer (JJA)", "(e) Fall (SON)"]
months = [range(1, 13), [12, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11] ]
season_to_months = dict(zip(seasons, months))
cru_var_name = "tmp"
temp_obs_data_store, temp_crcm4_data_store = _get_comparison_data(cru_var_name= cru_var_name,
season_to_months=season_to_months)
x, y = polar_stereographic.xs, polar_stereographic.ys
i_array, j_array = _get_routing_indices()
x_min, x_max, y_min, y_max = plot_utils.get_ranges(x[i_array, j_array], y[i_array, j_array])
#get swe data
swe_obs_name = "swe"
swe_obs_data_store, swe_crcm4_data_store = _get_comparison_data_swe(swe_var_name= swe_obs_name,
season_to_months=season_to_months, start_date=datetime(1980,1,1), end_date=datetime(1997, 1, 1))
plot_utils.apply_plot_params(width_pt= None, font_size=9, aspect_ratio=2.5)
fig = plt.figure()
assert isinstance(fig, Figure)
gs = gridspec.GridSpec(2, 1)
basemap = polar_stereographic.basemap
swe_season = seasons[1]
temp_season = seasons[2]
var_names = [ "swe", cru_var_name]
the_seasons = [ swe_season, temp_season ]
labels = [ "(a) Winter (DJF)", "(b) Sping (MAM)"]
units = [ "mm", "$^{\\circ}{\\rm C}$" ]
data_stores = [
[swe_obs_data_store, swe_crcm4_data_store],
[temp_obs_data_store, temp_crcm4_data_store]
]
all_plot_axes = []
for i, season, var_name, store, label, unit in zip(xrange(len(seasons)), the_seasons, var_names, data_stores,
labels, units):
ax = fig.add_subplot(gs[i, 0])
all_plot_axes.append(ax)
assert isinstance(ax, Axes)
crcm4 = store[1][season]
obs = store[0][season]
delta = crcm4 - obs
if var_name == "tmp": delta -= 273.15
if var_name == "swe":
ax.annotate("(1979-1997)", (0.1, 0.1), xycoords = "axes fraction",
font_properties = FontProperties(weight = "bold"))
elif var_name == "tmp":
ax.annotate("(1970-1999)", (0.1, 0.1), xycoords = "axes fraction",
font_properties = FontProperties(weight = "bold"))
color_map = my_cm.get_red_blue_colormap(ncolors = 16, reversed=(var_name == "tmp"))
color_map.set_over("k")
color_map.set_under("k")
#delta = maskoceans(polar_stereographic.lons, polar_stereographic.lats, delta)
save = delta[i_array, j_array]
delta = np.ma.masked_all(delta.shape)
delta[i_array, j_array] = save
vmin = np.floor( np.min(save) )
vmax = np.ceil( np.max(save) )
decimals = 0 if var_name == "swe" else 1
round_func = lambda x: np.round(x, decimals= decimals)
bounds = plot_utils.get_boundaries_for_colobar(vmin, vmax, color_map.N, round_func= round_func)
bn = BoundaryNorm( bounds, color_map.N )
img = basemap.pcolormesh(x, y, delta, cmap = color_map, norm = bn)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", "8%", pad="3%")
fig.colorbar(img, cax = cax, boundaries = bounds, ticks = bounds)
ax.set_title(label)
cax.set_title(unit)
for the_ax in all_plot_axes:
the_ax.set_xlim(x_min, x_max)
the_ax.set_ylim(y_min, y_max)
basemap.drawcoastlines(ax = the_ax, linewidth = 0.1)
plot_utils.draw_meridians_and_parallels(basemap, step_degrees=30.0, ax = the_ax)
plot_basin_boundaries_from_shape(basemap, axes = the_ax, linewidth=0.4)
put_selected_stations(the_ax, basemap, i_array, j_array)
fig.tight_layout()
fig.savefig("swe_temp_biases.png")
def plot_data(data, i_array, j_array, name='AEX', title = None, digits = 1,
color_map = mpl.cm.get_cmap('RdBu_r'),
minmax = (None, None),
units = '%',
colorbar_orientation = 'vertical',
draw_colorbar = True,
basemap = None, axes = None,
impose_lower_limit = None, upper_limited = False
):
if name is not None:
plt.figure()
to_plot = np.ma.masked_all((n_cols, n_rows))
for index, i, j in zip( range(len(data)), i_array, j_array):
to_plot[i, j] = data[index]
print np.ma.min(data), np.ma.max(data)
# m.pcolor(xs, ys, to_plot, cmap = mpl.cm.get_cmap('RdBu_r'))
if basemap is None:
the_basemap = m
else:
the_basemap = basemap
image = the_basemap.pcolormesh(xs, ys, to_plot.copy(), cmap = color_map,
vmin = minmax[0],
vmax = minmax[1], ax = axes)
#ads to m fields basins and basins_info which contain shapes and information
# m.readshapefile('data/shape/contour_bv_MRCC/Bassins_MRCC_utm18', 'basins')
# m.scatter(xs, ys, c=to_plot)
plot_basin_boundaries_from_shape(m, axes=axes, linewidth = 2.1)
#the_basemap.drawrivers(linewidth = 0.5, ax = axes)
the_basemap.drawcoastlines(linewidth = 0.5, ax = axes)
plot_utils.draw_meridians_and_parallels(the_basemap, step_degrees = 30)
axes.set_title(title if title is not None else name)
#zoom_to_qc()
x_min, x_max, y_min, y_max = plot_utils.get_ranges(xs[i_array, j_array], ys[i_array, j_array])
axes.set_xlim(x_min, x_max)
axes.set_ylim(y_min, y_max)
if draw_colorbar:
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(axes)
cax = divider.append_axes("right", "8%", pad="3%")
int_ticker = LinearLocator(numticks = color_map.N + 1)
cb = axes.figure.colorbar(image, ticks = int_ticker,
orientation = colorbar_orientation,
format = '%d', cax = cax, ax=axes, drawedges = True)
cb.ax.set_title(units)
#cb.outline.remove()
bottom, top = cb.ax.get_ylim()
left, right = cb.ax.get_xlim()
print bottom, top, left, right
if impose_lower_limit is None:
new_bottom = min( np.min(data), 0 )
else:
new_bottom = impose_lower_limit
#new_bottom = np.floor( new_bottom / 10.0 ) * 10.0
new_bottom = np.abs((new_bottom - minmax[0]) / (float(minmax[1] - minmax[0])))
new_bottom = plot_utils.get_closest_tick_value(color_map.N + 1, new_bottom) - 1.0e-4
print new_bottom
cb.ax.set_ylim(bottom = new_bottom)
left, right = cb.ax.get_xlim()
bottom, top = cb.ax.get_ylim()
#cb.ax.set_xlim(left = 0, right = 0.8)
cb.outline.set_visible( False )
if upper_limited:
cl = cb.ax.get_yticklabels()
labels = []
for text in cl:
labels.append(text.get_text())
labels[-1] = '$\\geq$' + labels[-1]
cb.ax.set_yticklabels(labels)
