def plot_domain_for_different_margins(path, margins=None):
if not margins: margins = [20, 40, 60]
rpnObj = RPN(path)
lons2d, lats2d = rpnObj.get_longitudes_and_latitudes()
# projection parameters
lon_1 = -68
lat_1 = 52
lon_2 = 16.65
lat_2 = 0.0
rot_lat_lon = RotatedLatLon(lon1=lon_1, lat1=lat_1, lon2=lon_2, lat2=lat_2)
xll, yll = rot_lat_lon.toProjectionXY(lons2d[0, 0], lats2d[0, 0])
xur, yur = rot_lat_lon.toProjectionXY(lons2d[-1, -1], lats2d[-1, -1])
if xll < 0: xll += 360.0
if xur < 0: xur += 360.0
nx, ny = lons2d.shape
dx = (xur - xll) / float(nx - 1)
dy = (yur - yll) / float(ny - 1)
print(dx, dy)
print(xur, yur, xll, yll)
x1 = xll - dx / 2.0
y1 = yll - dy / 2.0
x2 = xur + dx / 2.0
y2 = yur + dy / 2.0
x1lon, y1lat = rot_lat_lon.toGeographicLonLat(x1, y1)
x2lon, y2lat = rot_lat_lon.toGeographicLonLat(x2, y2)
llcrnrlon, llcrnrlat = rot_lat_lon.toGeographicLonLat(x1 - dx, y1 - dx)
urcrnrlon, urcrnrlat = rot_lat_lon.toGeographicLonLat(x2 + dx, y2 + dx)
basemap = Basemap(projection="omerc",
lon_1=lon_1,
lat_1=lat_1,
lon_2=lon_2,
lat_2=lat_2,
llcrnrlon=llcrnrlon,
llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon,
urcrnrlat=urcrnrlat,
no_rot=True,
resolution="l")
basemap.drawcoastlines()
basemap.drawrivers()
x1, y1 = basemap(x1lon, y1lat)
x2, y2 = basemap(x2lon, y2lat)
# add rectangle for the grid 220x220
# r1 = Rectangle((x1, y1), x2-x1, y2-y1, facecolor="none", edgecolor="r", linewidth=5 )
ax = plt.gca()
assert isinstance(ax, Axes)
# xr1_label, yr1_label = rot_lat_lon.toGeographicLonLat(xur - 2 * dx, yll + 2 * dy)
# xr1_label, yr1_label = basemap( xr1_label, yr1_label )
# ax.annotate("{0}x{1}".format(nx, ny), xy = (xr1_label, yr1_label), va = "bottom", ha = "right", color = "r")
# assert isinstance(ax, Axes)
# ax.add_patch(r1)
margins_all = [0] + margins
for margin in margins_all:
# mfree = margin - 20
xlli = xll + margin * dx
ylli = yll + margin * dy
xuri = xur - margin * dx
yuri = yur - margin * dy
x1lon, y1lat = rot_lat_lon.toGeographicLonLat(xlli, ylli)
x2lon, y2lat = rot_lat_lon.toGeographicLonLat(xuri, yuri)
x1, y1 = basemap(x1lon, y1lat)
x2, y2 = basemap(x2lon, y2lat)
ri = Rectangle((x1, y1),
x2 - x1,
y2 - y1,
facecolor="none",
edgecolor="r",
linewidth=5)
ax.add_patch(ri)
xri_label, yri_label = rot_lat_lon.toGeographicLonLat(
xlli + 2 * dx, yuri - 2 * dy)
xri_label, yri_label = basemap(xri_label, yri_label)
ax.annotate("{0}x{1}\nmarg. = {2}".format(nx - margin * 2,
ny - margin * 2,
margin + 20),
xy=(xri_label, yri_label),
va="top",
ha="left",
color="k",
backgroundcolor="w")
plt.show()
def diagnose(station_ids=None, model_data_path=None):
manager = Crcm5ModelDataManager(samples_folder_path=model_data_path,
file_name_prefix="pm",
all_files_in_samples_folder=True,
need_cell_manager=True)
nx, ny = manager.lons2D.shape
rot_lat_lon = RotatedLatLon(lon1=-68, lat1=52, lon2=16.65, lat2=0.0)
x00, y00 = rot_lat_lon.toProjectionXY(manager.lons2D[0, 0],
manager.lats2D[0, 0])
x10, y10 = rot_lat_lon.toProjectionXY(manager.lons2D[1, 0],
manager.lats2D[1, 0])
x01, y01 = rot_lat_lon.toProjectionXY(manager.lons2D[0, 1],
manager.lats2D[0, 1])
dx = x10 - x00
dy = y01 - y00
print("dx, dy = {0}, {1}".format(dx, dy))
areas = rot_lat_lon.get_areas_of_gridcells(
dx, dy, nx, ny, y00, 1) #1 -since the index is starting from 1
print(areas[0, 0])
start_date = datetime(1986, 1, 1)
end_date = datetime(1986, 12, 31)
stations = cehq_station.read_station_data(selected_ids=station_ids,
start_date=start_date,
end_date=end_date)
stations.sort(key=lambda x: x.latitude, reverse=True)
for i, s in enumerate(stations):
fig = plt.figure()
#3 columns
gs = GridSpec(5,
3,
hspace=0.2,
wspace=0.2,
right=0.98,
left=0.1,
top=0.98)
model_ts = manager.get_streamflow_timeseries_for_station(
s, start_date=start_date, end_date=end_date, nneighbours=9)
print(model_ts.time[0], model_ts.time[-1])
i_model0, j_model0 = model_ts.metadata["ix"], model_ts.metadata["jy"]
mask = manager.get_mask_for_cells_upstream(i_model0, j_model0)
#hydrographs
ax = fig.add_subplot(gs[0, 0])
plot_streamflows(ax, s, model_ts)
#relative error
ax = fig.add_subplot(gs[1, 0])
plot_streamflow_re(ax, s, model_ts)
#directions
plot_directions_and_positions(fig.add_subplot(gs[:2, 1]),
s,
model_ts,
manager,
rot_lat_lon,
mask=mask)
#runoff
ax = fig.add_subplot(gs[2, 0])
plot_runoff(ax, manager, areas, model_ts, mask=mask)
#runoff from gldas
ax = fig.add_subplot(gs[2, 1])
#plot_gldas_runoff(ax, manager, areas, model_ts, mask = mask)
#temperature
ax_temp = fig.add_subplot(gs[3, 0])
ax_prec = fig.add_subplot(gs[4, 0])
plot_total_precip_and_temp_re_1d(ax_prec,
ax_temp,
manager,
rot_lat_lon,
areas,
model_ts,
mask=mask)
#swe timeseries
ax = fig.add_subplot(gs[3, 1])
plot_swe_timeseries(ax, manager, areas, model_ts, mask=mask)
#print np.where(mask == 1)
print("(i, j) = ({0}, {1})".format(model_ts.metadata["ix"],
model_ts.metadata["jy"]))
fig.savefig("diagnose_{0}_{1:.2f}deg.pdf".format(s.id, dx))
def plot_domain_for_different_margins(path, margins=None):
if not margins: margins = [20, 40, 60]
rpnObj = RPN(path)
lons2d, lats2d = rpnObj.get_longitudes_and_latitudes()
# projection parameters
lon_1 = -68
lat_1 = 52
lon_2 = 16.65
lat_2 = 0.0
rot_lat_lon = RotatedLatLon(lon1=lon_1, lat1=lat_1, lon2=lon_2, lat2=lat_2)
xll, yll = rot_lat_lon.toProjectionXY(lons2d[0, 0], lats2d[0, 0])
xur, yur = rot_lat_lon.toProjectionXY(lons2d[-1, -1], lats2d[-1, -1])
if xll < 0: xll += 360.0
if xur < 0: xur += 360.0
nx, ny = lons2d.shape
dx = (xur - xll) / float(nx - 1)
dy = (yur - yll) / float(ny - 1)
print(dx, dy)
print(xur, yur, xll, yll)
x1 = xll - dx / 2.0
y1 = yll - dy / 2.0
x2 = xur + dx / 2.0
y2 = yur + dy / 2.0
x1lon, y1lat = rot_lat_lon.toGeographicLonLat(x1, y1)
x2lon, y2lat = rot_lat_lon.toGeographicLonLat(x2, y2)
llcrnrlon, llcrnrlat = rot_lat_lon.toGeographicLonLat(x1 - dx, y1 - dx)
urcrnrlon, urcrnrlat = rot_lat_lon.toGeographicLonLat(x2 + dx, y2 + dx)
basemap = Basemap(projection="omerc",
lon_1=lon_1, lat_1=lat_1,
lon_2=lon_2, lat_2=lat_2,
llcrnrlon=llcrnrlon, llcrnrlat=llcrnrlat,
urcrnrlon=urcrnrlon, urcrnrlat=urcrnrlat, no_rot=True, resolution="l")
basemap.drawcoastlines()
basemap.drawrivers()
x1, y1 = basemap(x1lon, y1lat)
x2, y2 = basemap(x2lon, y2lat)
# add rectangle for the grid 220x220
# r1 = Rectangle((x1, y1), x2-x1, y2-y1, facecolor="none", edgecolor="r", linewidth=5 )
ax = plt.gca()
assert isinstance(ax, Axes)
# xr1_label, yr1_label = rot_lat_lon.toGeographicLonLat(xur - 2 * dx, yll + 2 * dy)
# xr1_label, yr1_label = basemap( xr1_label, yr1_label )
# ax.annotate("{0}x{1}".format(nx, ny), xy = (xr1_label, yr1_label), va = "bottom", ha = "right", color = "r")
# assert isinstance(ax, Axes)
# ax.add_patch(r1)
margins_all = [0] + margins
for margin in margins_all:
# mfree = margin - 20
xlli = xll + margin * dx
ylli = yll + margin * dy
xuri = xur - margin * dx
yuri = yur - margin * dy
x1lon, y1lat = rot_lat_lon.toGeographicLonLat(xlli, ylli)
x2lon, y2lat = rot_lat_lon.toGeographicLonLat(xuri, yuri)
x1, y1 = basemap(x1lon, y1lat)
x2, y2 = basemap(x2lon, y2lat)
ri = Rectangle((x1, y1), x2 - x1, y2 - y1, facecolor="none", edgecolor="r", linewidth=5)
ax.add_patch(ri)
xri_label, yri_label = rot_lat_lon.toGeographicLonLat(xlli + 2 * dx, yuri - 2 * dy)
xri_label, yri_label = basemap(xri_label, yri_label)
ax.annotate("{0}x{1}\nmarg. = {2}".format(nx - margin * 2, ny - margin * 2, margin + 20),
xy=(xri_label, yri_label),
va="top", ha="left", color="k", backgroundcolor="w")
plt.show()