def test_cell_data_fetcher_ranalangvatn(self):
gs = GridSpecification(32632, x0=704000, y0=7431000, dx=1000, dy=1000, nx=98, ny=105)
pwrplants = [236]
cdf = CellDataFetcher(catchment_type="regulated", identifier="POWER_PLANT_ID", grid_specification=gs,
id_list=pwrplants)
cd = cdf.fetch()
self.assertIsNotNone(cd)
self.assertIsNotNone(cd['cell_data'])
self.assertIsNotNone(cd['cell_data'][pwrplants[0]])
self.assertIsNotNone(cd['catchment_land_types'])
self.assertIsNotNone(cd['elevation_raster'])
def plot_region_model(self, catchment_type, identifier, x0, y0, dx, dy, nx, ny,
catch_indicies, station_ids, epsg_id):
grid_spec = GridSpecification(epsg_id, x0, y0, dx, dy, nx, ny)
cf = CellDataFetcher(catchment_type, identifier, grid_spec, id_list=catch_indicies)
print("Start fetching data")
cf.fetch()
print("Done, now preparing plot")
# Plot the extracted data
fig, ax = plt.subplots(1)
color_map = {"forest": 'g', "lake": 'b', "glacier": 'r', "cell": "0.75", "reservoir": "purple"}
extent = grid_spec.geometry[0], grid_spec.geometry[2], grid_spec.geometry[1], grid_spec.geometry[3]
ax.imshow(cf.elevation_raster, origin='upper', extent=extent, cmap=cm.gray)
for catchment_cells in iter(cf.cell_data.values()):
self.add_plot_polygons(ax, [cell["cell"] for cell in catchment_cells], color=color_map["cell"])
for catchment_land_type in iter(cf.catchment_land_types.values()):
for k, v in iter(catchment_land_type.items()):
self.add_plot_polygons(ax, v, color=color_map[k])
geometry = grid_spec.geometry
if station_ids is not None:
glr = GisLocationService()
stations = glr.get_locations(station_ids, epsg_id)
if stations:
points = stations.values()
ax.scatter([pt[0] for pt in points], [pt[1] for pt in points], alpha=0.5)
station_min_x = min([v[0] for v in points]) - 1000
station_max_x = max([v[0] for v in points]) + 1000
station_min_y = min([v[1] for v in points]) - 1000
station_max_y = max([v[1] for v in points]) + 1000
geometry[0] = min(station_min_x, geometry[0])
geometry[1] = min(station_min_y, geometry[1])
geometry[2] = max(station_max_x, geometry[2])
geometry[3] = max(station_max_y, geometry[3])
base_dir = path.join(shyftdata_dir, "repository", "arome_data_repository")
EPSG = grid_spec.epsg()
bbox = grid_spec.bounding_box(EPSG)
arome4 = AromeDataRepository(EPSG, base_dir, filename="arome_metcoop_default2_5km_*.nc",
bounding_box=bbox, allow_subset=True)
# data_names = ("temperature", "wind_speed", "precipitation", "relative_humidity","radiation")
# utc_period = api.UtcPeriod(
# api.Calendar().time(api.YMDhms(2015, 10, 1, 0, 0, 0)),
# api.Calendar().time(api.YMDhms(2015, 10, 2, 0, 0, 0))
# )
# arome_ts=arome4.get_timeseries(["temperature"],utc_period)
# arome_points=[gts.mid_point() for gts in arome_ts['temperature']]
# ax.scatter( [pt.x for pt in arome_points ],[pt.y for pt in arome_points],c=[pt.z for pt in arome_points],alpha=0.5,cmap='gray',s=100)#, facecolors=('r'))
ax.set_xlim(geometry[0], geometry[2])
ax.set_ylim(geometry[1], geometry[3])
plt.show()
def plot_region_model(self, catchment_type, identifier ,x0, y0, dx, dy, nx, ny,
catch_indicies, station_ids,epsg_id):
grid_spec = GridSpecification(epsg_id, x0, y0, dx, dy, nx, ny)
cf = CellDataFetcher(catchment_type, identifier, grid_spec, id_list=catch_indicies)
print( "Start fetching data")
cf.fetch()
print ("Done, now preparing plot")
# Plot the extracted data
fig, ax = plt.subplots(1)
color_map = {"forest": 'g', "lake": 'b', "glacier": 'r', "cell": "0.75", "reservoir": "purple"}
extent = grid_spec.geometry[0], grid_spec.geometry[2], grid_spec.geometry[1], grid_spec.geometry[3]
ax.imshow(cf.elevation_raster, origin='upper', extent=extent, cmap=cm.gray)
for catchment_cells in iter(cf.cell_data.values()):
self.add_plot_polygons(ax, [cell["cell"] for cell in catchment_cells], color=color_map["cell"])
for catchment_land_type in iter(cf.catchment_land_types.values()):
for k,v in iter(catchment_land_type.items()):
self.add_plot_polygons(ax, v, color=color_map[k])
geometry = grid_spec.geometry
if station_ids is not None:
glr = GisLocationService()
stations = glr.get_locations(station_ids, epsg_id)
if stations:
points = stations.values()
ax.scatter([pt[0] for pt in points], [pt[1] for pt in points], alpha=0.5)
station_min_x = min([v[0] for v in points]) - 1000
station_max_x = max([v[0] for v in points]) + 1000
station_min_y = min([v[1] for v in points]) - 1000
station_max_y = max([v[1] for v in points]) + 1000
geometry[0] = min(station_min_x, geometry[0])
geometry[1] = min(station_min_y, geometry[1])
geometry[2] = max(station_max_x, geometry[2])
geometry[3] = max(station_max_y, geometry[3])
base_dir = path.join(shyftdata_dir, "repository", "arome_data_repository")
EPSG = grid_spec.epsg_id
bbox = grid_spec.bounding_box(EPSG)
arome4 = AromeDataRepository(EPSG, base_dir, filename="arome_metcoop_default2_5km_*.nc",
bounding_box=bbox, allow_subset=True)
#data_names = ("temperature", "wind_speed", "precipitation", "relative_humidity","radiation")
utc_period = api.UtcPeriod(
api.Calendar().time(api.YMDhms(2015,10,1,0,0,0)),
api.Calendar().time(api.YMDhms(2015,10,2,0,0,0))
)
#arome_ts=arome4.get_timeseries(["temperature"],utc_period)
#arome_points=[gts.mid_point() for gts in arome_ts['temperature']]
#ax.scatter( [pt.x for pt in arome_points ],[pt.y for pt in arome_points],c=[pt.z for pt in arome_points],alpha=0.5,cmap='gray',s=100)#, facecolors=('r'))
ax.set_xlim(geometry[0], geometry[2])
ax.set_ylim(geometry[1], geometry[3])
plt.show()
def test_cell_data_fetcher_ranalangvatn(self):
gs = GridSpecification(32632,
x0=704000,
y0=7431000,
dx=1000,
dy=1000,
nx=98,
ny=105)
pwrplants = [236]
cdf = CellDataFetcher(catchment_type="regulated",
identifier="POWER_PLANT_ID",
grid_specification=gs,
id_list=pwrplants)
cd = cdf.fetch()
self.assertIsNotNone(cd)
self.assertIsNotNone(cd['cell_data'])
self.assertIsNotNone(cd['cell_data'][pwrplants[0]])
self.assertIsNotNone(cd['catchment_land_types'])
self.assertIsNotNone(cd['elevation_raster'])