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_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_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
