def main():
model = LatLonHolder()
station_manager = StationManager()
station_manager.read_stations_from_files_rivdis()
selected_stations = []
ij_list = []
composite_index = 0
for longitude, latitude in zip( model.longitudes, model.latitudes ):
the_station = station_manager.get_station_closest_to(longitude, latitude)
if the_station != None:
selected_stations.append(the_station)
ix = model.get_ix(composite_index)
iy = model.get_iy(composite_index)
ij_list.append([ix, iy])
composite_index += 1
for station in selected_stations:
print station.longitude, station.latitude, station.id
print ij_list
def plot_monthly_means_with_measurements(model_points):
years = mdates.YearLocator(5, month=1, day=1) # every year
months = mdates.MonthLocator() # every month
yearsFmt = mdates.DateFormatter("%Y")
plt.cla()
##holder of longitudes and latitudes of the model grid points
holder = LatLonHolder()
# Manager of the measurement data
station_manager = StationManager()
station_manager.read_stations_from_files_rivdis()
station_manager.read_data_from_files_hydat()
print station_manager.get_station_by_id("02NG005")
measure_stations = []
selected_model_points = []
for model_point in model_points:
lon, lat = holder.get_lon_lat(model_point.ix, model_point.iy)
the_station = station_manager.get_station_closest_to(lon, lat)
if the_station != None:
measure_stations.append(the_station)
model_point.set_longitude(lon)
model_point.set_latitude(lat)
selected_model_points.append(model_point)
fig = plt.figure(figsize=(18, 18))
num_cols = 2
num_rows = len(selected_model_points) / num_cols
if len(selected_model_points) % num_cols > 0:
num_rows += 1
inches_per_pt = 1.0 / 72.27 # Convert pt to inch
golden_mean = (sqrt(5) - 1.0) / 2.0 # Aesthetic ratio
fig_width = 100.0 * inches_per_pt # width in inches
fig_height = fig_width * golden_mean # height in inches
fig_size = [fig_width, 10 * fig_height]
params = {
"axes.labelsize": 14,
"text.fontsize": 14,
"font.size": 14,
"legend.fontsize": 14,
"xtick.labelsize": 14,
"ytick.labelsize": 14,
}
print "fig_size = ", fig_size
pylab.rcParams.update(params)
assert len(selected_model_points) > 0
model_dates = selected_model_points[0].get_monthly_dates_sorted()
for i, model_point in enumerate(selected_model_points):
ax = plt.subplot(num_rows, num_cols, i + 1)
# plot model data
values = model_point.get_monthly_means_sorted_by_date()
ax.plot(model_dates, values, label="model")
# plot measurement data
values = measure_stations[i].get_values_for_dates(model_dates)
ax.plot(model_dates, values, "-", label="measurements", lw=3)
plt.title(model_point.__str__() + "\n" + measure_stations[i].__str__())
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.legend()
# ax.xaxis.set_minor_locator(months)
fig.subplots_adjust(wspace=0.4)
# fig.autofmt_xdate()
fig.savefig("comparison.png")
pass
def main():
"""
plots 2D domain map
"""
lons = read_lon_lat(path_to_longitudes, subtract_360=True)
lats = read_lon_lat(path_to_latitudes)
cell_lat_lons = get_cell_lats_lons(path_to_infocell)
cell_lons = []
cell_lats = []
for pair in cell_lat_lons:
cell_lons.append(pair[0])
cell_lats.append(pair[1])
inches_per_pt = 1.0 / 72.27 # Convert pt to inch
golden_mean = (sqrt(5) - 1.0) / 2.0 # Aesthetic ratio
fig_width = 1500 * inches_per_pt # width in inches
fig_height = fig_width * golden_mean # height in inches
fig_size = [fig_width, fig_height]
params = {
"axes.labelsize": 14,
"text.fontsize": 14,
"legend.fontsize": 14,
"xtick.labelsize": 16,
"ytick.labelsize": 16,
"figure.figsize": fig_size,
}
pylab.rcParams.update(params)
lat_min = min(lats)
lat_max = max(lats)
lon_max = max(lons)
lon_min = min(lons)
print lon_min, lon_max
print lat_min, lat_max
m = Basemap(
projection="npstere",
resolution="i",
lon_0=(lon_min + lon_max) / 2.0,
lat_0=(lat_min + lat_max) / 2.0,
boundinglat=40,
lat_ts=60,
llcrnrlon=lon_min,
llcrnrlat=lat_min,
urcrnrlon=lon_max,
urcrnrlat=lat_max,
)
projected_lons, projected_lats = m(lons, lats)
# m.scatter(projected_lons, projected_lats, color = "red", s = 0.1)
projected_lons, projected_lats = m(cell_lons, cell_lats)
m.scatter(projected_lons, projected_lats, color="green", s=45, marker="s")
# show station positions
manager = StationManager()
manager.read_stations_from_files_rivdis()
station_lons = []
station_lats = []
for station in manager.stations:
station_lons.append(station.longitude)
station_lats.append(station.latitude)
print len(station_lons)
projected_lons, projected_lats = m(station_lons, station_lats)
m.scatter(projected_lons, projected_lats, color="red", s=45, marker="o")
m.drawcoastlines()
m.drawcountries()
# m.fillcontinents(color='white',lake_color='blue', zorder=0)
m.drawmeridians(np.arange(lon_min, lon_max, 10), labels=[0, 0, 0, 0])
m.drawparallels(np.arange(lat_min, lat_max, 10), labels=[0, 0, 0, 0])
y_min, y_max = plt.ylim()
x_min, x_max = plt.xlim()
plt.ylim(y_min, y_max / 2.0)
plt.xlim(x_max / 4.0, 0.9 * x_max)
plt.savefig("domain.png")
