def _get_routing_indices():
"""
Used for the plot domain centering
"""
from data import data_select
i_indices, j_indices = data_select.get_indices_from_file(path = "data/streamflows/hydrosheds_euler9/aex_discharge_1970_01_01_00_00.nc")
return i_indices, j_indices
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')
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')
if __name__ == "__main__":
i_indices, j_indices = data_select.get_indices_from_file()
data = data_select.get_field_from_file('data/test_data/divided.nc', 'water_discharge')
data = np.max(data, axis = 0)
plot_data(data, i_indices, j_indices, name = 'discharge_scaling', title = 'Discharge response \n to runoff doubling')
print "Hello World"
def main():
start_year = 1970
end_year = 1999
stations = cehq_station.read_station_data(folder="data/cehq_measure_data_all")
stations = list( itertools.ifilter( lambda s: s.is_natural, stations) )
for s in stations:
s.delete_data_after_year(end_year)
s.delete_data_before_year(start_year)
pass
stations = list( itertools.ifilter(lambda s: s.get_num_of_years_with_continuous_data() >= 10, stations) )
s = stations[0]
assert isinstance(s, Station)
#stations = list( itertools.ifilter(lambda s: s.is_natural, stations) )
x, y = polar_stereographic.lons, polar_stereographic.lats
basemap = polar_stereographic.basemap
x, y = basemap(x,y)
sx = [s.longitude for s in stations]
sy = [s.latitude for s in stations]
sx, sy = basemap(sx, sy)
#read model data
model_file_path = "data/streamflows/hydrosheds_euler9/aex_discharge_1970_01_01_00_00.nc"
acc_area = data_select.get_field_from_file(path=model_file_path,
field_name="drainage")
i_indices, j_indices = data_select.get_indices_from_file(path=model_file_path)
lons_1d = data_select.get_field_from_file(path=model_file_path, field_name="longitude")
lats_1d = data_select.get_field_from_file(path=model_file_path, field_name="latitude")
x1d, y1d, z1d = lat_lon.lon_lat_to_cartesian(lons_1d, lats_1d)
kdtree = KDTree(zip(x1d, y1d, z1d))
print "Id: 4 DA (km2) <-> 4 dist (km) <-> 4 (i,j)"
#basemap.scatter(sx, sy, c = "r", zorder = 5)
for s, isx, isy in zip( stations, sx, sy ):
assert isinstance(s, Station)
plt.annotate(s.id, xy=(isx, isy),
bbox = dict(facecolor = 'white'), weight = "bold", font_properties = FontProperties(size=0.5))
#get model drainaige areas for the four closest gridcells to the station
x0, y0, z0 = lat_lon.lon_lat_to_cartesian(s.longitude, s.latitude)
dists, indices = kdtree.query([x0, y0, z0], k = 4)
dists /= 1000
print("{0}: {1:.1f}; {2:.1f}; {3:.1f}; {4:.1f} <-> {5:.1f}; {6:.1f}; {7:.1f}; {8:.1f} <-> {9};{10};{11};{12}".format(
"{0} (S_DA = {1:.1f})".format(s.id, s.drainage_km2),
float(acc_area[indices[0]]),
float(acc_area[indices[1]]),
float(acc_area[indices[2]]),
float(acc_area[indices[3]]),
float( dists[0] ),
float(dists[1]),
float(dists[2]),
float(dists[3]),
"({0}, {1})".format(i_indices[indices[0]] + 1, j_indices[indices[0]] + 1),
"({0}, {1})".format(i_indices[indices[1]] + 1, j_indices[indices[1]] + 1),
"({0}, {1})".format(i_indices[indices[2]] + 1, j_indices[indices[2]] + 1),
"({0}, {1})".format(i_indices[indices[3]] + 1, j_indices[indices[3]] + 1)
))
basemap.drawcoastlines(linewidth=0.5)
xmin, xmax = min(sx), max(sx)
ymin, ymax = min(sy), max(sy)
marginx = (xmax - xmin) * 0.1
marginy = (ymax - ymin) * 0.1
xmin -= marginx * 1.5
xmax += marginx * 2
ymin -= marginy
ymax += marginy * 2
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.tight_layout()
basin_boundaries.plot_basin_boundaries_from_shape(basemap=basemap, plotter=plt, linewidth=1)
plt.savefig("10yr_cont_stations_natural_fs0.5.pdf")
#plt.show()
pass
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)
