def plot_mean_extreme_flow(folder_path = "data/streamflows/hydrosheds_euler9",
member_ids = None,
file_name_pattern = "%s_discharge_1970_01_01_00_00.nc",
out_file_name = "annual_means.png",
high = True,
start_month = 1, end_month = 12
):
"""
Plot mean extreme (1-day high or 15-day low) flow over time
"""
if member_ids is None:
return
i_indices = None
j_indices = None
times = None
x_min, x_max = None, None
y_min, y_max = None, None
the_extreme_list = []
for i, the_id in enumerate( member_ids ):
fName = file_name_pattern % the_id
fPath = os.path.join(folder_path, fName)
if not i:
data, times, i_indices, j_indices = data_select.get_data_from_file(fPath)
interest_x = x[i_indices, j_indices]
interest_y = y[i_indices, j_indices]
x_min, x_max, y_min, y_max = _get_limits(interest_x = interest_x, interest_y = interest_y)
else:
data = data_select.get_field_from_file(path = fPath)
assert data is not None, "i = %d " % i
if high:
extremes = data_select.get_list_of_annual_maximums_for_domain(data, times,
start_month = start_month,
end_month = end_month)
else:
extremes = data_select.get_list_of_annual_minimums_for_domain(data, times,
event_duration = timedelta(days = 15),
start_month = start_month,
end_month = end_month
)
the_extreme_list.append(np.mean(extremes, axis = 0))
print "shape of extremes list ", np.array(the_extreme_list).shape
plot_utils.apply_plot_params(aspect_ratio=0.8)
plt.figure()
plt.subplots_adjust(hspace = 0.1, wspace = 0.3)
for k, the_extreme_mean in enumerate(the_extreme_list):
plt.subplot( 2, len(member_ids) // 2 + 1 , k + 1)
to_plot = np.ma.masked_all(x.shape)
for the_index, i, j in zip( xrange(len(i_indices)), i_indices, j_indices):
to_plot[i, j] = the_extreme_mean[the_index]
basemap.pcolormesh(x, y, to_plot.copy(), cmap = mpl.cm.get_cmap(name = "jet_r", lut = 18), vmin = 0,
vmax = 1.5)
basemap.drawcoastlines(linewidth = 0.5)
plt.colorbar(ticks = LinearLocator(numticks = 7), format = "%.2e")
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.savefig(out_file_name)
#plot cv for the extremes (here for performance, no need to again fetch the extremes)
max_value = 0.1
plot_utils.apply_plot_params(width_pt=600)
plt.figure()
extreme_means = np.array( the_extreme_list )
mu = np.mean(extreme_means, axis=0)
sigma = np.std(extreme_means,axis=0)
cv = sigma / mu
to_plot = np.ma.masked_all(x.shape)
for the_index, i, j in zip( xrange(len(i_indices)), i_indices, j_indices):
to_plot[i, j] = cv[the_index]
basemap.pcolormesh(x, y, to_plot.copy(), cmap = mpl.cm.get_cmap(name = "jet_r", lut = 30), vmin = 0,
vmax = max_value)
basemap.drawcoastlines(linewidth = 0.5)
plt.colorbar(ticks = LinearLocator(numticks = 11), format = "%.1e")
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.savefig("cv_" + out_file_name)
pass
def compare_means(member = 'aet', my_data_path = '',
start_date = datetime(1961, 1, 1, 0, 0),
end_date = datetime(1990, 12, 31, 0, 0)):
streamflows, times, i_array, j_array = data_select.get_data_from_file(my_data_path)
event_duration = timedelta(days = 1)
my_data = data_select.get_list_of_annual_maximums_for_domain(streamflows, times,
start_date = start_date, end_date = end_date,
start_month = 1, end_month = 12,
event_duration = event_duration)
data_path = 'data/streamflows/Vincent_annual_max/mapHIGH_{0}.txt'.format(member)
v = VincentMaximumsReader(data_path = data_path)
the_format = '{0}: i = {1}, j = {2}, min = {3}, max = {4}, mean = {5}'
vmeans = []
vmins = []
vmaxs = []
# my_data = 500 * np.ones((10,547))
for i, j, the_index in zip(i_array, j_array, range(my_data.shape[1])):
data = my_data[:, the_index]
print the_format.format('Sasha', i, j, np.min(data), np.max(data), np.mean(data))
data = v.get_data_at(i + 1, j + 1)
print the_format.format('Vincent', i, j, np.min(data), np.max(data), np.mean(data))
vmeans.append(np.mean(data))
vmins.append(np.min(data))
vmaxs.append(np.max(data))
print '=' * 30
#scatter plot for means
plt.subplots_adjust(hspace = 0.5)
plt.subplot(2,2,1)
plt.title('annual maximums, \n average for each grid point', override)
plt.scatter( vmeans , np.mean(my_data, axis = 0), linewidth = 0)
plt.xlabel('Vincent')
plt.ylabel('Sasha')
x = plt.xlim()
plt.plot(x,x, color = 'k')
plt.grid(True)
#scatter plot for minimums
plt.subplot(2,2,2)
plt.title('annual maximums, \n minimum for each grid point', override)
plt.scatter( vmins , np.min(my_data, axis = 0), linewidth = 0)
plt.xlabel('Vincent')
plt.ylabel('Sasha')
x = plt.xlim()
plt.plot(x,x, color = 'k')
plt.grid(True)
#scatter plot for minimums
plt.subplot(2,2,3)
plt.title('annual maximums, \n maximum for each grid point', override)
plt.scatter( vmaxs , np.max(my_data, axis = 0), linewidth = 0)
plt.xlabel('Vincent')
plt.ylabel('Sasha')
x = plt.xlim()
plt.plot(x,x, color = 'k')
plt.grid(True)
plt.savefig('{0}_scatter_max.png'.format(member), bbox_inches = 'tight')
