def plot_cv_for_return_levels_current_and_future_and_change():
low_periods = [2, 5]
high_periods = [10, 30]
extreme_types = ['high', 'low']
extreme_type_to_periods = dict(list(zip(extreme_types, [high_periods, low_periods])))
i_indices, j_indices = data_select.get_indices_from_file()
current_cvs = {}
future_cvs = {}
for extreme_type in extreme_types:
current_cvs[extreme_type] = {} #to save return level fields for a given extreme type
future_cvs[extreme_type] = {}
for return_period in extreme_type_to_periods[extreme_type]:
c_rl_fields = np.zeros((len(members.current_ids), len(i_indices)))
f_rl_fields = np.zeros((len(members.current_ids), len(i_indices)))
for member_num, the_member in enumerate(members.current_ids):
pars_current = csfb.get_pars_for_member_and_type(the_member, level_type = extreme_type)
pars_future = csfb.get_pars_for_member_and_type(members.current2future[the_member], level_type = extreme_type)
#calculate changes for each pair of members and each position
npos = len(pars_current)
for pos, pc, pf in zip(range(npos), pars_current, pars_future):
if extreme_type == 'high':
c = gevfit.get_high_ret_level_stationary(pc, return_period)
f = gevfit.get_high_ret_level_stationary(pf, return_period)
else:
c = gevfit.get_low_ret_level_stationary(pc, return_period)
f = gevfit.get_low_ret_level_stationary(pf, return_period)
c_rl_fields[member_num, pos] = c
f_rl_fields[member_num, pos] = f
pass
current_cvs[extreme_type][return_period] = np.std( c_rl_fields, axis = 0 ) / np.mean( c_rl_fields, axis = 0 )
future_cvs[extreme_type][return_period] = np.std( f_rl_fields, axis = 0 ) / np.mean( f_rl_fields, axis = 0 )
#do plotting
_plot_map_as_subplots(i_indices, j_indices, current_cvs, title = " Current Climate, CV ")
plt.savefig('cv_for_return_levels_current.png')
_plot_map_as_subplots(i_indices, j_indices, future_cvs, title = "Future Climate, CV")
plt.savefig('cv_for_return_levels_future.png')
pass
def main():
plot_utils.apply_plot_params(width_pt=None, font_size=9, aspect_ratio=2.5)
gs = mpl.gridspec.GridSpec(3,2)
key_data_list = get_key_data_list()
i_list, j_list = data_select.get_indices_from_file()
subplot_count = 0
for the_type in ["high", "low"]:
for time_window in ["current", "future"]:
selected_data = None
for data in key_data_list:
if data.time_window == time_window and data.type == the_type:
selected_data = data
break
row = subplot_count // 2
col = subplot_count % 2
plt.subplot(gs[row, col])
csfb.plot(selected_data.p_values, i_list, j_list,
polar_stereographic.xs, polar_stereographic.ys,
units = "", basemap = polar_stereographic.basemap,
minmax = (0, 0.25), title = "", # "{0} climate, {1} flow".format(selected_data.time_window, selected_data.type),
colorbar_label_format="%.2f", color_map = mpl.cm.get_cmap("jet", 5), upper_limited=True
)
subplot_count += 1
#TODO:add 2 subplots for mean values
pc = kw_test_for_means()
plt.subplot(gs[2,0])
csfb.plot(pc, i_list, j_list,
polar_stereographic.xs, polar_stereographic.ys,
units = "", basemap = polar_stereographic.basemap,
minmax = (0, 0.25), #title = "{0} climate, {1} flow".format("current", "mean"),
colorbar_label_format="%.2f", color_map = mpl.cm.get_cmap("jet", 5), upper_limited=True
)
pf = kw_test_for_means(current_climate=False)
plt.subplot(gs[2, 1])
csfb.plot(pf, i_list, j_list,
polar_stereographic.xs, polar_stereographic.ys,
units = "", basemap = polar_stereographic.basemap,
minmax = (0, 0.25), #title = "{0} climate, {1} flow".format("future", "mean"),
colorbar_label_format="%.2f", color_map = mpl.cm.get_cmap("jet", 5), upper_limited=True
)
plt.tight_layout()
plt.savefig("p_values_kruskalwallis.png")
def plot_high_flows(period = 10,
imagefile = 'figure.png',
pars_set = None,
indices_file = 'data/streamflows/hydrosheds_euler9/aex_discharge_1970_01_01_00_00.nc'):
print('generating %s ' % imagefile)
plt.clf()
levs = []
i_list, j_list = data_select.get_indices_from_file(indices_file)
#iterate through all grid points
for pars in pars_set:
lev = get_high_ret_level_stationary(pars, period)
if lev < 0:
print('period = ', period)
print('pars = ', pars)
assert False, 'in plot_high_flows'
assert lev >= 0, pars
levs.append( lev )
to_plot = np.ma.masked_all(xs.shape)
for lev, i, j in zip(levs, i_list, j_list):
assert np.isfinite(lev)
if isinf(lev):
print(lev)
to_plot[i,j] = lev
nticks = 15
color_map = mpl.cm.get_cmap('RdBu',nticks)
int_ticker = LinearLocator(numticks = color_map.N + 1)
m.pcolormesh(xs, ys, to_plot.copy(), cmap = color_map)
plt.title('high flow, return period is {0}'.format(period))
boundaries.plot_basin_boundaries_from_shape(m, plt, linewidth = 0.5)
m.drawcoastlines()
# plot_directions(data_mask = to_plot)
plt.colorbar( ticks = int_ticker, format = "%.1f" )
zoom_to_qc()
plt.savefig(imagefile, bbox_inches = 'tight')
def plot_data(data = None, imagefile = 'image.png',
units = 'm**3/s',
minmax = (None, None),
indices_file = 'data/streamflows/hydrosheds_euler9/aex_discharge_1970_01_01_00_00.nc',
color_map = mpl.cm.get_cmap('RdBu', 20),
ticks_locator = LinearLocator(),
i_list = None, j_list = None, title = ''):
if imagefile is not None:
plt.clf()
if i_list is None or j_list is None:
i_list, j_list = data_select.get_indices_from_file(indices_file)
to_plot = np.ma.masked_all(xs.shape)
for i, j, value in zip(i_list, j_list, data):
to_plot[i, j] = value
plt.title(title)
m.pcolormesh(xs,ys,to_plot.copy(), cmap = color_map, edgecolors = 'None',
antialiased = True, vmin = minmax[0], vmax = minmax[1])
m.drawcoastlines()
boundaries.plot_basin_boundaries_from_shape(m, plotter = plt, linewidth = 0.5)
draw_meridians_and_parallels(m, 10)
# plot_directions(data_mask = to_plot)
cb = plt.colorbar(ticks = ticks_locator, format = "%.1f")
cb.ax.set_ylabel(units)
x1, x2, y1, y2 = plot_utils.get_ranges(xs[i_list, j_list], ys[i_list, j_list])
plt.xlim(x1, x2)
plt.ylim(y1, y2)
if imagefile is not None:
plt.savefig(imagefile, bbox_inches = 'tight')
def plot_signal_to_noise_ratio():
low_periods = [2, 5]
high_periods = [10, 30]
plot_utils.apply_plot_params(width_pt=None, font_size=9, aspect_ratio=2.5)
extreme_types = ['high', 'low']
extreme_type_to_periods = dict(list(zip(extreme_types, [high_periods, low_periods])))
i_indices, j_indices = data_select.get_indices_from_file()
i_subplot = 0
#plt.subplots_adjust(wspace = 0.1)
cMap = mpl.cm.get_cmap('jet', 3)
cMap.set_over(color = '#FF8C00')
gs = gridspec.GridSpec(3,2)
for extreme_type in extreme_types:
for return_period in extreme_type_to_periods[extreme_type]:
changes = np.zeros((len(members.current_ids), len(i_indices)))
for member_num, the_member in enumerate(members.current_ids):
pars_current = csfb.get_pars_for_member_and_type(the_member, level_type = extreme_type)
pars_future = csfb.get_pars_for_member_and_type(members.current2future[the_member], level_type = extreme_type)
#calculate changes for each pair of members and each position
npos = len(pars_current)
for pos, pc, pf in zip(range(npos), pars_current, pars_future):
if extreme_type == 'high':
c = gevfit.get_high_ret_level_stationary(pc, return_period)
f = gevfit.get_high_ret_level_stationary(pf, return_period)
else:
c = gevfit.get_low_ret_level_stationary(pc, return_period)
f = gevfit.get_low_ret_level_stationary(pf, return_period)
changes[member_num, pos] = f - c
pass
#calculate mean and stdev of the obtained changes
the_mean = np.mean(changes, axis = 0)
the_std = np.std(changes, axis = 0)
#change if you want signal to noise ratio, currently it is cv (coefficient of variation 1/(signal-to-noise-ratio))
the_values = the_std / np.abs(the_mean)
print(the_values.min(), the_values.max())
#the_values = the_mean
to_plot = np.ma.masked_all(csfb.xs.shape)
max_value = 1.5
for i, j, value, dev in zip(i_indices, j_indices, the_values, the_std):
to_plot[i, j] = value
#shaded = np.ma.masked_where(to_plot != 0, shaded)
#to_plot = np.ma.masked_where(to_plot == 0, to_plot)
plot_axes = plt.subplot(gs[i_subplot // 2, i_subplot % 2])
i_subplot += 1
print('just before plotting')
basin_boundaries.plot_basin_boundaries_from_shape(csfb.basemap, plotter = plt, linewidth = 1.)
image = csfb.basemap.pcolormesh(csfb.xs, csfb.ys, to_plot, vmin = 0, vmax = max_value, cmap = cMap,
ax = plot_axes)
csfb.basemap.drawcoastlines(linewidth = 0.2)
plot_axes.set_title('T = {0}-year'.format(return_period))
x_min, x_max, y_min, y_max = plot_utils.get_ranges(csfb.xs[i_indices, j_indices], csfb.ys[i_indices, j_indices])
plot_axes.set_xlim(x_min, x_max)
plot_axes.set_ylim(y_min, y_max)
divider = make_axes_locatable(plot_axes)
cax = divider.append_axes("right", "8%", pad="3%")
# extend choices are "both", "min", "max" and "neither"
cb = plt.colorbar(image, extend = 'max',
format = "%.1f", drawedges = True, cax = cax)
cb.set_ticks(LinearLocator(numticks = 4))
cb.outline.set_visible(False)
#plt.show()
plt.tight_layout()
plt.savefig('cv_for_changes.png')
def stationary():
#data_folder = 'data/streamflows/hydrosheds_euler9'
data_folder = "data/streamflows/narccap_ccsm-crcm"
current_data_path_pattern = '%s_discharge_1970_01_01_00_00.nc'
future_data_path_pattern = '%s_discharge_2041_01_01_00_00.nc'
i_list, j_list = data_select.get_indices_from_file()
current_ids = ["ccsm-crcm-current"]
future_ids = ["ccsm-crcm-future"]
current2future = dict(list(zip(current_ids, future_ids)))
current_start_date = datetime(1970, 1, 1, 0, 0)
current_end_date = datetime(1999, 11, 23,0, 0)
future_start_date = datetime(2041, 1, 1, 0, 0)
future_end_date = datetime(2070, 11, 23,0, 0)
high_return_periods = [10]
high_start_month = 3
high_end_month = 7
high_event_duration = timedelta(days = 1)
low_return_periods = [2]
low_start_month = 1
low_end_month = 5
low_event_duration = timedelta(days = 15)
all_return_periods = []
all_return_periods.extend(high_return_periods)
all_return_periods.extend(low_return_periods)
plot_return_levels = False
extreme_types = ['low', 'high']
#calculate parameters of the gev distriution for each member
#calculate and plot return levels
for extreme_type in extreme_types:
start_month = low_start_month if extreme_type == 'low' else high_start_month
end_month = low_end_month if extreme_type == 'low' else high_end_month
event_duration = low_event_duration if extreme_type == 'low' else high_event_duration
for current_id in current_ids:
param_file = 'gev_params_stationary'
param_file += '_' + current_id + '_' + extreme_type
data_file = current_data_path_pattern % current_id
data_file = os.path.join(data_folder, data_file)
pars_set = optimize_stationary_for_period_and_all_cells(data_file,
paramfile = param_file ,
high_flow = (extreme_type == 'high'),
start_month = start_month,
end_month = end_month,
start_date = current_start_date,
end_date = current_end_date,
event_duration = event_duration)
if plot_return_levels:
for period in low_return_periods:
plot_low_flows(period = period,
imagefile = '%drlevel_%s_stationary_%s.png' % (period, extreme_type, current_id),
pars_set = pars_set)
print('Finished optimizing for current climate')
for future_id in future_ids:
param_file = 'gev_params_stationary'
param_file += '_' + future_id + '_' + extreme_type
data_file = future_data_path_pattern % future_id
data_file = os.path.join(data_folder, data_file)
pars_set = optimize_stationary_for_period_and_all_cells(data_file,
paramfile = param_file ,
high_flow = (extreme_type == 'high'),
start_month = start_month,
end_month = end_month,
start_date = future_start_date,
end_date = future_end_date,
event_duration = event_duration)
if plot_return_levels:
for period in low_return_periods:
plot_low_flows(period = period, imagefile = '%drlevel_%s_stationary_%s.png' % (period, extreme_type ,future_id),
pars_set = pars_set)
print('Finished optimizing for future climate')
print('Finished calculating return levels !!!')
plot_mean_changes = True
if not plot_mean_changes:
return
###############################current climate return levels
print('Calculating mean high flow return levels for current climate ...')
current_rl_means = {}
future_rl_means = {}
the_type_to_periods = {'high': high_return_periods, 'low': low_return_periods}
keys = []
for the_type, periods in the_type_to_periods.items():
for period in periods:
k = TypePeriodKey()
k.type = the_type
k.return_period = period
keys.append(k)
for key in keys:
current_rl_means[key] = []
future_rl_means[key] = []
#collect return levels for corresponding type(high , low) and return period for each member and then take mean
for current_id in current_ids:
for key in keys:
future_id = current2future[current_id]
the_field_current = get_levels_for_type_and_id(current_id, return_period = key.return_period, type = key.type)
the_field_future = get_levels_for_type_and_id(future_id, return_period = key.return_period, type = key.type)
assert isinstance(the_field_current, np.ndarray)
assert isinstance(the_field_future, np.ndarray)
indices = np.where((the_field_current > 0) & (the_field_future >= 0) )
to_plot = np.ma.masked_all(the_field_current.shape)
to_plot[indices] = (the_field_future[indices] - the_field_current[indices]) / the_field_current[indices] * 100.0
file_name = '{0}-{1}_{2}_{3}yr_change.png'.format(current_id, future_id, key.type, key.return_period)
delta = np.max(np.abs(to_plot[indices]))
delta = min(100.0, delta)
plot_data(data = to_plot, imagefile = file_name,
units = '%', minmax = (-125, 125), color_map = my_cm.get_red_blue_colormap(ncolors = 16),
title = '{0}-{1}, change, {2}, return period: {3}'.format(current_id,
future_id, key.type, key.return_period)
)
future_rl_means[key].append(the_field_future)
current_rl_means[key].append(the_field_current)
for key in keys:
current_rl_means[key] = np.array(current_rl_means[key])
current_rl_means[key] = np.ma.masked_where(current_rl_means[key] < 0, current_rl_means[key])
future_rl_means[key] = np.array(future_rl_means[key])
future_rl_means[key] = np.ma.masked_where(future_rl_means[key] < 0, future_rl_means[key])
current_rl_means[key] = np.ma.mean( current_rl_means[key] , axis = 0)
future_rl_means[key] = np.ma.mean( future_rl_means[key] , axis = 0)
# plt.figure()
# plt.subplot(2,1,1)
# plt.title('current mean')
# plot_data(current_rl_means[key], imagefile = None)
#
# plt.subplot(2,1,2)
# plt.title('future mean')
# plot_data(future_rl_means[key], imagefile = None)
#
# plt.savefig('means_%s_%dyr_rl.png' % (key.type, key.return_period))
###################################################
####Calculate differences between future and current return levels for 10 and 30 year
#### return period.
####
plot_utils.apply_plot_params(width_pt=None, font_size=9, aspect_ratio=2.5)
fig = plt.figure()
assert isinstance(fig, Figure)
gs = GridSpec(3,2, height_ratios=[1,1,1])
for i, key in enumerate(keys):
current = current_rl_means[key]
future = future_rl_means[key]
indices = np.where((current > 0) & (future >= 0))
#to_plot = np.ma.masked_all(current.shape)
to_plot = (future[indices] - current[indices]) / current[indices] * 100.0
delta = np.max(np.abs(to_plot))
min_change = np.min(to_plot)
if not key.type == "high":
delta = 100
lower_limit = 0 if min_change >= 0 else np.floor(min_change / 10.0) * 10
else:
delta = 50
lower_limit = np.floor(min_change / 10.0 ) * 10
fig.add_subplot(gs[i // 2, i % 2])
csfb.plot(to_plot, i_list, j_list, xs, ys,
imagefile = None, #'%s_%dyr_mean_change.png' % (key.type, key.return_period),
units = '%', minmax = (-delta, delta),
color_map = my_cm.get_red_blue_colormap(ncolors = 20), #mpl.cm.get_cmap('RdYlBu',20),
title = '{0}-year {1} flow'.format( key.return_period, key.type),
colorbar_tick_locator = LinearLocator(numticks = 11), upper_limited=True,
impose_lower_limit= lower_limit, basemap=polar_stereographic.basemap
)
#gs.update()
plt.tight_layout(h_pad = 2)
fig.savefig("ccsm-crcm-rl-changes.png")
def plot_results():
cc = "current"
fc = "future"
climate_types = [cc, fc]
folder_path = 'data/streamflows/hydrosheds_euler9/'
file_path = os.path.join(folder_path, "aex_discharge_1970_01_01_00_00.nc")
i_indices, j_indices = data_select.get_indices_from_file(file_path)
xs = polar_stereographic.xs
ys = polar_stereographic.ys
basemap = polar_stereographic.basemap
hi = "high"
lo = "low"
extreme_types = [hi, lo]
base_std_name = "{0}_std_dev_{1}"
base_par_name = {
cc : "_".join(["{0}"] + members.current_ids),
fc : "_".join(["{0}"] + members.future_ids)
}
extreme_to_return_periods = {
hi : get_high_return_periods(),
lo : get_low_return_periods()
}
sig_coefs = [1.96
# , 1.645
]
sig_levels = ["95 %"
# , "90 %"
]
gs = gridspec.GridSpec(3,2)
for extreme in extreme_types:
pars_path_current = base_par_name[cc].format(extreme)
std_path_current = base_std_name.format(extreme, cc)
pars_path_future = base_par_name[fc].format(extreme)
std_path_future = base_std_name.format(extreme, fc)
pars_current = pickle.load(open(pars_path_current))
stds_current = pickle.load(open(std_path_current))
pars_future = pickle.load(open(pars_path_future))
stds_future = pickle.load(open(std_path_future))
return_periods = extreme_to_return_periods[extreme]
#plot_utils.apply_plot_params(font_size=15, width_pt=900, aspect_ratio=2.5)
#plt.figure()
delta = 50 if extreme == hi else 100
for row, ret_period in enumerate( return_periods ):
#calculate changes in return levels
func = lambda x: gevfit.get_high_ret_level_stationary(x, ret_period)
rl_c = list(map(func, pars_current))
rl_f = list(map(func, pars_future))
rl_c = np.array(rl_c)
rl_f = np.array(rl_f)
std_c = stds_current[ret_period]
std_f = stds_future[ret_period]
in_odf = (std_c > 0) & (std_f > 0) & (rl_c > 0) & (rl_f >= 0)
change = np.ma.masked_all(rl_c.shape)
change[in_odf] = (rl_f[in_odf] - rl_c[in_odf]) / rl_c[in_odf] * 100.0
min_change = np.min((rl_f - rl_c) / rl_c * 100.0)
if min_change >= 0:
low_limit = 0
elif min_change > -10:
low_limit = -10
else:
low_limit = np.floor(min_change / 10.0) * 10
print("min change = {0}, low limit = {1}".format(min_change, low_limit))
for sig_coef, sig_name in zip(sig_coefs, sig_levels):
significance = np.ma.masked_all(rl_c.shape)
sig_cond = (sig_coef * (std_c + std_f) < np.abs(rl_f - rl_c)) & in_odf
significance[~sig_cond] = 0#fill with gray non signifcant areas
change1 = np.ma.masked_where(~sig_cond, change)
if extreme == hi:
plt.subplot(gs[row, 0])
else:
plt.subplot(gs[row, 1])
csfb.plot(change1 , i_indices, j_indices, xs, ys,
title = 'T = {0}-year'.format( ret_period ),
color_map = mycolors.get_red_blue_colormap(ncolors = 10),
units = '%',
basemap = basemap, minmax = (-delta, delta),
colorbar_label_format = '%d',
upper_limited = True,
colorbar_tick_locator = LinearLocator(numticks = 11),
not_significant_mask = significance,
show_colorbar = True, impose_lower_limit=low_limit
)
#subplot_count += 1
plt.tight_layout()
plt.savefig("rl_of_merged_change.png")
pass
def plot_naveed_data(path = 'data/data_txt_naveed/3_all_for_plotting.csv'):
"""
plotting data calculated by Naveed
"""
f = open(path)
lines = f.readlines()
f.close()
#skip header
while len(lines) > 0:
line = lines.pop(0)
if line.startswith('stationSrI'):
break
folder_path = 'data/streamflows/hydrosheds_euler9/'
i_indices, j_indices = data_select.get_indices_from_file(folder_path + 'aex_discharge_1970_01_01_00_00.nc')
return_periods = [2,10,30]
level_cols = [4,5,6]
sig95_cols = [9,10,11]
sig90_cols = [14, 15, 16]
sets = list(zip(level_cols, return_periods, sig95_cols, sig90_cols))
subplot_count = 1
for lev_col, period, sig95_col, sig90_col in sets:
for sig_col in [sig95_col, sig90_col]:
ret_lev = np.array(get_column(lev_col, lines))
plt.subplot(3,2,subplot_count)
significance = 1 - np.array(get_column(sig_col, lines))
ret_lev = np.ma.masked_where(significance == 1, ret_lev)
significance *= 0.75
significance = np.ma.masked_where(significance == 0, significance)
delta = 50
sig_level = '95%' if sig_col == sig95_col else '90%'
plot(ret_lev , i_indices, j_indices, xs, ys,
title = 'T = %d year, conf. (%s)' % (period, sig_level),
color_map = mycolors.get_red_blue_colormap(ncolors = 16), units = '%',
basemap = basemap, minmax = (-delta, delta),
colorbar_label_format = '%d',
upper_limited = True, colorbar_tick_locator = LinearLocator(numticks = 9),
not_significant_mask = significance, show_colorbar = (subplot_count == 6)
)
subplot_count += 1
plt.savefig('3_all_for_plotting.pdf', bbox_inches = 'tight')
return
plt.figure()
b = Basemap(resolution = 'i')
plt.subplot(2,1,1)
medians = get_column(1, lines)
to_plot = np.ma.masked_all(polar_stereographic.lons.shape)
for i, j, med in zip(i_indices, j_indices, medians):
to_plot[i,j] = med
b.pcolormesh(polar_stereographic.lons, polar_stereographic.lats, to_plot)
b.drawcoastlines()
plt.colorbar(shrink = 0.5)
cond = ~to_plot.mask
min_lon = polar_stereographic.lons[cond].min()
max_lon = polar_stereographic.lons[cond].max()
min_lat = polar_stereographic.lats[cond].min()
max_lat = polar_stereographic.lats[cond].max()
marginx = 0.05 * (max_lon - min_lon)
marginy = 0.05 * (max_lat - min_lat)
plt.xlim(min_lon - marginx, max_lon + marginx)
plt.ylim(min_lat - marginy, max_lat + marginy)
plt.title('median change (%)')
plt.subplot(2,1,2)
pvalues = get_column(2, lines)
to_plot = np.ma.masked_all(polar_stereographic.lons.shape)
for i, j, pvalue in zip(i_indices, j_indices, pvalues):
to_plot[i,j] = pvalue
b.pcolormesh(polar_stereographic.lons, polar_stereographic.lats, to_plot)
b.drawcoastlines()
plt.colorbar(shrink = 0.5)
plt.title('p-value for median change')
marginx = 0.05 * (max_lon - min_lon)
marginy = 0.05 * (max_lat - min_lat)
plt.xlim(min_lon - marginx, max_lon + marginx)
plt.ylim(min_lat - marginy, max_lat + marginy)
plt.savefig('median.pdf', bbox_inches = 'tight')
pass
def plot_naveed_troubled_points(path = 'data/data_txt_naveed/TroubledGridCells_AtLeast3Sims.csv'):
f = open(path)
lines = f.readlines()
if len(lines) == 1:
lines = lines[0].split('\r')
f.close()
#skip header
while len(lines) > 0:
line = lines.pop(0)
if 'stationsri' in line.lower():
break
print(lines)
folder_path = 'data/streamflows/hydrosheds_euler9/'
i_indices, j_indices = data_select.get_indices_from_file(folder_path + 'aex_discharge_1970_01_01_00_00.nc')
cols = [1,2,3]
names = ['C', 'F', 'Both']
color_map = ListedColormap(['r','b', 'g'])
#plt.subplots_adjust(hspace = 0.0001)
for c, name in zip(cols, names):
plt.subplot(2,2,c)
to_plot = np.ma.masked_all(xs.shape)
data = get_column(c, lines)
print(data)
for i, j, v in zip(i_indices, j_indices, data):
if v: #no color for 0
to_plot[i, j] = v
else:
to_plot[i, j] = 3
basemap.pcolormesh(xs, ys, to_plot.copy(), cmap = color_map,
shading = 'flat', rasterized = False )
plot_basin_boundaries_from_shape(basemap, plotter = plt, linewidth = 1, edge_color = 'k')
basemap.drawcoastlines(linewidth = 0.5)
plot_utils.draw_meridians_and_parallels(basemap, step_degrees = 30)
plt.title(name)
ymin, ymax = plt.ylim()
plt.ylim(ymin + 0.05 * (ymax - ymin) , ymax * 0.25)
xmin, xmax = plt.xlim()
plt.xlim(xmin + (xmax - xmin) * 0.55, 0.72*xmax)
cb = plt.colorbar(shrink = 0.5, format = '%d')
cb.set_ticks([1.25, 2, 2.75])
cb.set_ticklabels([1,2,3])
plt.savefig('TroubledGridCells_AtLeast3Sims.pdf', bbox_inches = 'tight')
pass
def calculate_and_plot(return_period = 10,
return_level_function = ret_level_getters[0], ax = None):
save_fig_to_file = (ax is None)
if return_level_function == gevfit.get_high_ret_level_stationary:
level_type = 'high'
else:
level_type = 'low'
fig = plt.figure()
assert isinstance(fig, Figure)
save_to_txt = False
current_ids = ["ccsm-crcm-current"] #members.current_ids
future_ids = ["ccsm-crcm-future"] #members.future_ids
current2future = dict(list(zip(current_ids, future_ids)))
#folder_path = 'data/streamflows/hydrosheds_euler9/'
folder_path = "data/streamflows/narccap_ccsm-crcm"
coord_file = os.path.join(folder_path, '{0}_discharge_1970_01_01_00_00.nc'.format(current_ids[0]))
i_indices, j_indices = data_select.get_indices_from_file(coord_file)
significance_counter = None
#plt.subplots_adjust(left = 0., hspace = 0.2, wspace = 0.2)
labels = ["", "(b)", "(c)", "(d)", "(e)", "(f)"]
##for querying high flow data for saving to text file
current_query = None
future_query = None
current_highs = None
future_highs = None
if level_type == 'high' and save_to_txt:
high_period_start_month = 3
high_period_end_month = 7
current_start_date = datetime(1970,1,1,0,0)
current_end_date = datetime(1999,12,31,0,0)
future_start_date = datetime(2041,1,1,0,0)
future_end_date = datetime(2070,12,31,0,0)
future_query = QueryObject()
future_query.start_date = future_start_date
future_query.end_date = future_end_date
future_query.event_duration = timedelta(days = 1)
future_query.start_month = high_period_start_month
future_query.end_month = high_period_end_month
current_query = QueryObject()
current_query.start_date = current_start_date
current_query.end_date = current_end_date
current_query.event_duration = timedelta(days = 1)
current_query.start_month = high_period_start_month
current_query.end_month = high_period_end_month
gs = gridspec.GridSpec(3,2)
current_id_to_changes = {}
all_current = []
all_future = []
all_stds_current = []
all_stds_future = []
for k, current_id in enumerate(current_ids):
if level_type == 'high' and save_to_txt:
current_path = folder_path + '{0}_discharge_1970_01_01_00_00.nc'.format(current_id)
future_path = folder_path + '{0}_discharge_2041_01_01_00_00.nc'.format(current2future[current_id])
current_data, times_current, x_indices, y_indices = data_select.get_data_from_file(current_path)
future_data, times_future, x_indices, y_indices = data_select.get_data_from_file(future_path)
current_highs = data_select.get_period_maxima_query(current_data, times_current, current_query)
future_highs = data_select.get_period_maxima_query(future_data, times_future, future_query)
#get current return levels
pars_list = get_pars_for_member_and_type(current_id, level_type)
return_levels_current = np.zeros(len(pars_list))
for pos, pars in enumerate(pars_list):
return_levels_current[pos] = return_level_function(pars, return_period)
stdevs_current = get_stdevs_for_member_and_type(current_id, level_type)[return_period]
#get future return levels
future_id = current2future[current_id]
pars_list = get_pars_for_member_and_type(future_id, level_type)
return_levels_future = np.zeros(len(pars_list))
for pos, pars in enumerate(pars_list):
return_levels_future[pos] = return_level_function(pars, return_period)
stdevs_future = get_stdevs_for_member_and_type(future_id, level_type)[return_period]
change = return_levels_future - return_levels_current
if significance_counter is None:
significance_counter = np.zeros( change.shape )
print('minmax(std_current)')
print(np.min(stdevs_current), np.max(stdevs_current))
print('minmax(std_future)')
print(np.min(stdevs_future), np.max(stdevs_future))
print('min max min abs(rl_current - rl_future)')
the_delta = np.abs(return_levels_future - return_levels_current)
print(np.min(the_delta), np.max(the_delta), np.mean(the_delta))
#stdev = -1 - stands for undefined value
condition = np.logical_and(np.abs(change) > 1.96 * ( stdevs_current + stdevs_future ),
(stdevs_current >= 0) & (stdevs_future >= 0)
& (return_levels_current > 0)
)
sign_index = np.where(condition)
significance_counter[sign_index] += 1
print(len(sign_index[0]))
all_current.append(return_levels_current)
all_future.append(return_levels_future)
all_stds_current.append(stdevs_current)
all_stds_future.append(stdevs_future)
change /= return_levels_current
change *= 100.0
min_change = np.min(change)
print(return_levels_current[change == min_change], return_levels_future[change == min_change], min_change)
if not level_type == "high":
delta = 100
lower_limit = 0 if min_change >= 0 else np.floor(min_change / 10.0) * 10
else:
delta = 50
lower_limit = np.floor(min_change / 10.0 ) * 10
not_significant = np.zeros(change.shape)
not_significant = np.ma.masked_where(condition, not_significant)
if level_type == 'high':
assert np.all(return_levels_current > 0)
assert np.all(stdevs_current >= 0)
assert np.all(stdevs_future >= 0)
#temp change to condition
#change = np.ma.masked_where(np.logical_not(condition), change)
print('Plotting: current %s, future %s' % (current_id, future_id))
current_id_to_changes[current_id] = change
if ax is None:
ax = fig.add_subplot(gs[k // 2, k % 2])
plot(change , i_indices, j_indices, xs, ys,
title = "{0}-year {1} flow".format(return_period, level_type), label = labels[k],
color_map = mycolors.get_red_blue_colormap(ncolors = 20), units = '%',
basemap = basemap, minmax = (-delta, delta),
colorbar_label_format = '%d',
upper_limited = True, colorbar_tick_locator = LinearLocator(numticks = 11),
not_significant_mask = not_significant
, impose_lower_limit=lower_limit, ax= ax
)
if return_period == 10 and level_type == 'high' and save_to_txt:
txt_saver.save_to_file_rls_and_sign(current_id, return_period,
return_levels_current, return_levels_future,
stdevs_current, stdevs_future,
condition, current_highs, future_highs)
plt.subplot(gs[2,1])
plot_sign_count = False
plot_significance = True
if plot_sign_count: #if plotting significance count
significance_counter = np.ma.masked_where(significance_counter == 0, significance_counter)
plot(significance_counter, i_indices, j_indices, xs, ys,
title = 'Significance Count', label = labels[5], minmax = (1,6),
color_map = mycolors.get_sign_count_cmap(ncolors = 5), basemap = basemap,
colorbar_tick_locator = MaxNLocator(nbins = 5),
colorbar_label_format = '%d'
)
#TODO plot +/-
plus_change = None
minus_change = None
for current_id, the_change in current_id_to_changes.items():
if plus_change is None:
plus_change = (the_change > 0)
minus_change = (the_change < 0)
else:
plus_change = np.logical_and(the_change > 0, plus_change)
minus_change = np.logical_and(the_change < 0, minus_change)
#should be at least one member with significant changes
plus_change = np.logical_and(plus_change, significance_counter > 0)
minus_change = np.logical_and(minus_change, significance_counter > 0)
x_interest = xs[i_indices, j_indices]
y_interest = ys[i_indices, j_indices]
x_plus = x_interest[plus_change]
y_plus = y_interest[plus_change]
x_minus = x_interest[minus_change]
y_minus = y_interest[minus_change]
basemap.scatter(x_plus, y_plus, marker = "+", color = "m", s = 15, zorder = 5, linewidth = 1)
if len(x_minus) > 0:
basemap.scatter(x_minus, y_minus, marker = "d", zorder = 6)
else:
#plot ensemble mean
all_current = np.array( all_current )
all_future = np.array( all_future )
all_stds_current = np.array( all_stds_current )
all_stds_future = np.array( all_stds_future )
mean_current = np.mean(all_current, axis = 0)
mean_future = np.mean(all_future, axis = 0)
mean_stds_current = np.mean( all_stds_current, axis = 0 )
mean_stds_future = np.mean( all_stds_future, axis = 0 )
min_change = np.min((mean_future - mean_current)/mean_current * 100.0)
if not level_type == "high":
delta = 100
lower_limit = 0 if min_change >= 0 else np.floor(min_change / 10.0) * 10
else:
delta = 100
lower_limit = np.floor(min_change / 10.0 ) * 10
not_significant = np.absolute(mean_future - mean_current) <= 1.96 * (mean_stds_current + mean_stds_future)
not_significant = not_significant.astype(int)
print(" sum(not_significant) = ", np.sum(not_significant))
not_significant = np.ma.masked_where(~(not_significant == 1), not_significant)
not_significant *= 0.0
if not plot_significance:
not_significant = None
plot((mean_future - mean_current) / mean_current * 100.0, i_indices, j_indices, xs, ys,
title = "", label = labels[-1],
color_map = mycolors.get_red_blue_colormap(ncolors = 20), units = '%',
basemap = basemap, minmax = (-delta, delta),
colorbar_label_format = '%d',
upper_limited = True, colorbar_tick_locator = LinearLocator(numticks = 11),
not_significant_mask = not_significant,
impose_lower_limit = lower_limit
)
pass
if save_fig_to_file:
plt.tight_layout()
plt.savefig('%d_%s_change_rl.png' % (return_period, level_type))
