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_diff(folder = "data/streamflows/hydrosheds_euler9",
plot_f_and_c_means_separately = False):
"""
Plot difference between the means for future and current climate
"""
file_name = None
for f_name in os.listdir(folder):
if f_name.startswith( "aex" ):
file_name = f_name
#get indices of interest
x_indices, y_indices = data_select.get_indices_from_file(path=os.path.join(folder, file_name))
##get significance and percentage changes
#signific_vector, change = bootstrap_for_mean.get_significance_for_change_in_mean_over_months()
#signific_vector, change = ttest_for_mean_of_merged.get_significance_and_changes_for_months()
signific_vector, change = bootstrap_for_mean_merged.get_significance_for_change_in_mean_of_merged_over_months()
signific_vector = signific_vector.astype(int)
#start plotting (f-c)/c * 100
plt.subplots_adjust(hspace = 0.2)
#significance level 5%
plot_axes = plt.subplot(1,1,1)
plot_data( change,
x_indices, y_indices, name = None,
color_map = my_cm.get_red_blue_colormap(ncolors = 8),
#mpl.cm.get_cmap('RdBu', 16),
minmax = (-40, 40),
title = '', axes=plot_axes
)
color_map = mpl.cm.get_cmap(name="gray", lut=3)
signific_vector = np.ma.masked_where(signific_vector == 1, signific_vector)
plot_data(signific_vector, x_indices, y_indices, name = None, title="",
minmax = (-1, 1), color_map=color_map, draw_colorbar=False, axes=plot_axes)
plt.tight_layout()
plt.savefig('future-current(sign).png')
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 calculate_seasonal_changes_in_mean_stfl_and_plot(folder_path = 'data/streamflows/hydrosheds_euler9',
months = None, label = "",
cb_axes = None,
plot_axes = None,
impose_lower_limit = None,
upper_limited = False,
minmax = None
):
"""
"""
if months is None:
print "please specify months"
return
fileName = None
for fName in os.listdir(folder_path):
if fName.startswith( "aex" ):
fileName = fName
i_indices, j_indices = data_select.get_indices_from_file(path = os.path.join(folder_path, fileName))
xs = polar_stereographic.xs
ys = polar_stereographic.ys
basemap = polar_stereographic.basemap
#get mean changes along with its significance
#ensemble mean
significance, change = bootstrap_for_mean.get_significance_for_change_in_mean_over_months(months=months)
#significance, change = ttest_for_mean_of_merged.get_significance_and_changes_for_months(months=months)
#merged
#significance, change = bootstrap_for_mean_merged.get_significance_for_change_in_mean_of_merged_over_months(months= months)
#plot mean change
print "plotting"
plot_axes.set_title('{0}'.format(label))
calculate_mean_map.plot_data(change, i_indices, j_indices, minmax = minmax, title=label, name = None,
color_map = my_cm.get_red_blue_colormap(ncolors = 10),
draw_colorbar=True, basemap=basemap, axes=plot_axes,
impose_lower_limit = impose_lower_limit,
upper_limited = upper_limited
)
plot_significance = True
if plot_significance:
to_plot = np.ma.masked_all(xs.shape)
significance = significance.astype(int)
significance = np.ma.masked_where(significance == 1, significance)
for the_significance, i, j in zip(significance, i_indices, j_indices):
to_plot[i, j] = the_significance
basemap.pcolormesh( xs, ys, to_plot.copy(), cmap = mpl.cm.get_cmap(name = "gray", lut = 3),
vmin = -1, vmax = 1, ax = plot_axes)
