def polyline_to_narr_grid(polyline_latitudes_deg, polyline_longitudes_deg,
dilation_distance_metres):
"""Converts polyline to binary image over NARR grid.
V = number of vertices in polyline
:param polyline_latitudes_deg: length-V numpy array of latitudes (deg N).
:param polyline_longitudes_deg: length-V numpy array of longitudes (deg E).
:param dilation_distance_metres: Dilation distance. This gives fronts a
non-infinitesimal width, which allows them to be more than one grid cell
(pixel) wide. This accounts for spatial uncertainty in the placement of
fronts.
:return: binary_image_matrix: See documentation for `_check_frontal_image`.
This will be a 277-by-349 matrix, to match the dimensions of the NARR
grid.
"""
polyline_x_coords_metres, polyline_y_coords_metres = (
nwp_model_utils.project_latlng_to_xy(
latitudes_deg=polyline_latitudes_deg,
longitudes_deg=polyline_longitudes_deg,
model_name=nwp_model_utils.NARR_MODEL_NAME))
grid_point_x_coords_metres, grid_point_y_coords_metres = (
nwp_model_utils.get_xy_grid_points(
model_name=nwp_model_utils.NARR_MODEL_NAME))
rows_in_polyline, columns_in_polyline = _polyline_to_grid_points(
polyline_x_coords_metres=polyline_x_coords_metres,
polyline_y_coords_metres=polyline_y_coords_metres,
grid_point_x_coords_metres=grid_point_x_coords_metres,
grid_point_y_coords_metres=grid_point_y_coords_metres)
num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions(
model_name=nwp_model_utils.NARR_MODEL_NAME)
binary_image_matrix = _grid_points_to_binary_image(
rows_in_object=rows_in_polyline,
columns_in_object=columns_in_polyline,
num_grid_rows=num_grid_rows,
num_grid_columns=num_grid_columns)
return dilate_binary_narr_image(
binary_image_matrix=binary_image_matrix.astype(int),
dilation_distance_metres=dilation_distance_metres)
def test_projection_grid130(self):
"""Ensures approx correctness of Lambert projection for NCEP 130 grid.
This method ensures that x-y coordinates for all grid points can be
generated accurately from lat-long coordinates. Specifically, the mean
and max distance errors must be <= 100 and 500 metres, respectively.
NOTE: This test relies on several methods, so it is not a unit test.
"""
num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions(
model_name=nwp_model_utils.RAP_MODEL_NAME,
grid_id=nwp_model_utils.ID_FOR_130GRID)
(grid_point_lng_vector_deg, grid_point_lat_vector_deg) = numpy.loadtxt(
GRID130_LATLNG_FILE_NAME, unpack=True)
grid_point_lat_matrix_deg = numpy.reshape(
grid_point_lat_vector_deg, (num_grid_rows, num_grid_columns))
grid_point_lng_matrix_deg = numpy.reshape(
grid_point_lng_vector_deg, (num_grid_rows, num_grid_columns))
grid_point_x_matrix_metres, grid_point_y_matrix_metres = (
nwp_model_utils.project_latlng_to_xy(
grid_point_lat_matrix_deg, grid_point_lng_matrix_deg,
model_name=nwp_model_utils.RAP_MODEL_NAME,
grid_id=nwp_model_utils.ID_FOR_130GRID))
(expected_grid_point_x_matrix_metres,
expected_grid_point_y_matrix_metres) = (
nwp_model_utils.get_xy_grid_point_matrices(
model_name=nwp_model_utils.RAP_MODEL_NAME,
grid_id=nwp_model_utils.ID_FOR_130GRID))
x_error_matrix_metres = (
grid_point_x_matrix_metres - expected_grid_point_x_matrix_metres)
y_error_matrix_metres = (
grid_point_y_matrix_metres - expected_grid_point_y_matrix_metres)
distance_error_matrix_metres = numpy.sqrt(
x_error_matrix_metres ** 2 + y_error_matrix_metres ** 2)
self.assertTrue(numpy.mean(
distance_error_matrix_metres) <= MAX_MEAN_DISTANCE_ERROR_RAP_METRES)
self.assertTrue(numpy.max(
distance_error_matrix_metres) <= MAX_MAX_DISTANCE_ERROR_RAP_METRES)
def test_projection_extended221(self):
"""Ensures approx correctness of Lambert proj for extended 221 grid."""
num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions(
model_name=nwp_model_utils.NARR_MODEL_NAME,
grid_name=nwp_model_utils.NAME_OF_221GRID)
unique_longitudes_deg, unique_latitudes_deg = numpy.loadtxt(
NARR_LATLNG_FILE_NAME, unpack=True)
latitude_matrix_deg = numpy.reshape(unique_latitudes_deg,
(num_grid_rows, num_grid_columns))
longitude_matrix_deg = numpy.reshape(unique_longitudes_deg,
(num_grid_rows, num_grid_columns))
x_matrix_metres, y_matrix_metres = nwp_model_utils.project_latlng_to_xy(
latitudes_deg=latitude_matrix_deg,
longitudes_deg=longitude_matrix_deg,
model_name=nwp_model_utils.NARR_MODEL_NAME,
grid_name=nwp_model_utils.NAME_OF_221GRID)
expected_x_matrix_metres, expected_y_matrix_metres = (
nwp_model_utils.get_xy_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME,
grid_name=nwp_model_utils.NAME_OF_EXTENDED_221GRID))
expected_x_matrix_metres = expected_x_matrix_metres[100:-100, 100:-100]
expected_y_matrix_metres = expected_y_matrix_metres[100:-100, 100:-100]
expected_x_matrix_metres -= expected_x_matrix_metres[0, 0]
expected_y_matrix_metres -= expected_y_matrix_metres[0, 0]
x_error_matrix_metres = x_matrix_metres - expected_x_matrix_metres
y_error_matrix_metres = y_matrix_metres - expected_y_matrix_metres
distance_error_matrix_metres = numpy.sqrt(x_error_matrix_metres**2 +
y_error_matrix_metres**2)
self.assertTrue(
numpy.mean(distance_error_matrix_metres) <=
MAX_MEAN_DISTANCE_ERROR_NARR_METRES)
self.assertTrue(
numpy.max(distance_error_matrix_metres) <=
MAX_MAX_DISTANCE_ERROR_NARR_METRES)
def test_projection_grid252(self):
"""Ensures approx correctness of Lambert projection for NCEP 252 grid.
See documentation for test_projection_grid130.
"""
num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions(
model_name=nwp_model_utils.RAP_MODEL_NAME,
grid_id=nwp_model_utils.ID_FOR_252GRID)
(grid_point_lng_vector_deg, grid_point_lat_vector_deg) = numpy.loadtxt(
GRID252_LATLNG_FILE_NAME, unpack=True)
grid_point_lat_matrix_deg = numpy.reshape(
grid_point_lat_vector_deg, (num_grid_rows, num_grid_columns))
grid_point_lng_matrix_deg = numpy.reshape(
grid_point_lng_vector_deg, (num_grid_rows, num_grid_columns))
grid_point_x_matrix_metres, grid_point_y_matrix_metres = (
nwp_model_utils.project_latlng_to_xy(
grid_point_lat_matrix_deg, grid_point_lng_matrix_deg,
model_name=nwp_model_utils.RAP_MODEL_NAME,
grid_id=nwp_model_utils.ID_FOR_252GRID))
(expected_grid_point_x_matrix_metres,
expected_grid_point_y_matrix_metres) = (
nwp_model_utils.get_xy_grid_point_matrices(
model_name=nwp_model_utils.RAP_MODEL_NAME,
grid_id=nwp_model_utils.ID_FOR_252GRID))
x_error_matrix_metres = (
grid_point_x_matrix_metres - expected_grid_point_x_matrix_metres)
y_error_matrix_metres = (
grid_point_y_matrix_metres - expected_grid_point_y_matrix_metres)
distance_error_matrix_metres = numpy.sqrt(
x_error_matrix_metres ** 2 + y_error_matrix_metres ** 2)
self.assertTrue(numpy.mean(
distance_error_matrix_metres) <= MAX_MEAN_DISTANCE_ERROR_RAP_METRES)
self.assertTrue(numpy.max(
distance_error_matrix_metres) <= MAX_MAX_DISTANCE_ERROR_RAP_METRES)
def _project_fronts_latlng_to_narr(front_line_table):
"""Projects fronts from lat-long to NARR (x-y) coordinates.
P = number of points in a given front
:param front_line_table: See doc for `fronts_io.write_polylines_to_file`.
:return: front_line_table: Same as input, but with the following extra
columns.
front_line_table.x_coords_metres: length-P numpy array of x-coordinates.
front_line_table.y_coords_metres: length-P numpy array of y-coordinates.
"""
num_fronts = len(front_line_table.index)
projection_object = nwp_model_utils.init_model_projection(
nwp_model_utils.NARR_MODEL_NAME)
x_coords_by_front_metres = [numpy.array([])] * num_fronts
y_coords_by_front_metres = [numpy.array([])] * num_fronts
for i in range(num_fronts):
if numpy.mod(i, 1000) == 0:
print('Have projected {0:d} of {1:d} fronts to NARR coordinates...'
).format(i, num_fronts)
(x_coords_by_front_metres[i],
y_coords_by_front_metres[i]) = nwp_model_utils.project_latlng_to_xy(
latitudes_deg=front_line_table[
front_utils.LATITUDES_COLUMN].values[i],
longitudes_deg=front_line_table[
front_utils.LONGITUDES_COLUMN].values[i],
projection_object=projection_object,
model_name=nwp_model_utils.NARR_MODEL_NAME)
print 'Projected all {0:d} fronts to NARR coordinates!'.format(num_fronts)
return front_line_table.assign(
**{
X_COORDS_COLUMN: x_coords_by_front_metres,
Y_COORDS_COLUMN: y_coords_by_front_metres
})
def _run():
"""Plots input example.
This is effectively the main method.
:return: figure_file_name: Path to output file (where the figure was saved).
"""
valid_time_unix_sec = time_conversion.string_to_unix_sec(
VALID_TIME_STRING, TIME_FORMAT)
front_file_name = fronts_io.find_file_for_one_time(
top_directory_name=TOP_FRONT_DIR_NAME,
file_type=fronts_io.POLYLINE_FILE_TYPE,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(front_file_name)
front_line_table = fronts_io.read_polylines_from_file(front_file_name)
num_narr_fields = len(NARR_FIELD_NAMES)
narr_matrix_by_field = [numpy.array([])] * num_narr_fields
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
this_directory_name = '{0:s}/earth_relative_wind'.format(
TOP_NARR_DIRECTORY_NAME)
else:
this_directory_name = TOP_NARR_DIRECTORY_NAME + ''
this_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=this_directory_name,
field_name=NARR_FIELD_NAMES[j],
pressure_level_mb=PRESSURE_LEVEL_MB,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
narr_matrix_by_field[j] = processed_narr_io.read_fields_from_file(
this_file_name)[0][0, ...]
narr_matrix_by_field[j] = utils.fill_nans(narr_matrix_by_field[j])
if NARR_FIELD_NAMES[j] == processed_narr_io.WET_BULB_THETA_NAME:
narr_matrix_by_field[j] = (narr_matrix_by_field[j] -
ZERO_CELSIUS_IN_KELVINS)
# (_, front_centroid_latitude_deg, front_centroid_longitude_deg
# ) = _find_nearest_front(
# front_line_table=front_line_table,
# query_latitude_deg=APPROX_FRONT_LATITUDE_DEG,
# query_longitude_deg=APPROX_FRONT_LONGITUDE_DEG)
front_centroid_latitude_deg = APPROX_FRONT_LATITUDE_DEG + 0.
front_centroid_longitude_deg = APPROX_FRONT_LONGITUDE_DEG + 0.
projection_object = nwp_model_utils.init_model_projection(
nwp_model_utils.NARR_MODEL_NAME)
these_x_metres, these_y_metres = nwp_model_utils.project_latlng_to_xy(
latitudes_deg=numpy.array([front_centroid_latitude_deg]),
longitudes_deg=numpy.array([front_centroid_longitude_deg]),
projection_object=projection_object,
model_name=nwp_model_utils.NARR_MODEL_NAME)
front_centroid_x_metres = these_x_metres[0]
front_centroid_y_metres = these_y_metres[0]
grid_spacing_metres, _ = nwp_model_utils.get_xy_grid_spacing(
model_name=nwp_model_utils.NARR_MODEL_NAME)
center_narr_row_index = int(
numpy.round(front_centroid_y_metres / grid_spacing_metres))
center_narr_column_index = int(
numpy.round(front_centroid_x_metres / grid_spacing_metres))
first_narr_row_index = center_narr_row_index - NUM_ROWS_IN_HALF_GRID
last_narr_row_index = center_narr_row_index + NUM_ROWS_IN_HALF_GRID
first_narr_column_index = (center_narr_column_index -
NUM_COLUMNS_IN_HALF_GRID)
last_narr_column_index = center_narr_column_index + NUM_COLUMNS_IN_HALF_GRID
for j in range(num_narr_fields):
narr_matrix_by_field[j] = narr_matrix_by_field[j][
first_narr_row_index:(last_narr_row_index + 1),
first_narr_column_index:(last_narr_column_index + 1)]
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=first_narr_row_index,
last_row_in_full_grid=last_narr_row_index,
first_column_in_full_grid=first_narr_column_index,
last_column_in_full_grid=last_narr_column_index,
resolution_string='i')
plotting_utils.plot_coastlines(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_countries(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_states_and_provinces(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_parallels(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lat_deg=-90.,
upper_right_lat_deg=90.,
parallel_spacing_deg=PARALLEL_SPACING_DEG)
plotting_utils.plot_meridians(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lng_deg=0.,
upper_right_lng_deg=360.,
meridian_spacing_deg=MERIDIAN_SPACING_DEG)
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
continue
min_colour_value = numpy.percentile(narr_matrix_by_field[j],
MIN_COLOUR_PERCENTILE)
max_colour_value = numpy.percentile(narr_matrix_by_field[j],
MAX_COLOUR_PERCENTILE)
nwp_plotting.plot_subgrid(
field_matrix=narr_matrix_by_field[j],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=THERMAL_COLOUR_MAP_OBJECT,
min_value_in_colour_map=min_colour_value,
max_value_in_colour_map=max_colour_value,
first_row_in_full_grid=first_narr_row_index,
first_column_in_full_grid=first_narr_column_index)
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=narr_matrix_by_field[j],
colour_map=THERMAL_COLOUR_MAP_OBJECT,
colour_min=min_colour_value,
colour_max=max_colour_value,
orientation='horizontal',
extend_min=True,
extend_max=True)
u_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.U_WIND_EARTH_RELATIVE_NAME)
v_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.V_WIND_EARTH_RELATIVE_NAME)
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=narr_matrix_by_field[u_wind_index],
v_wind_matrix_m_s01=narr_matrix_by_field[v_wind_index],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_full_grid=first_narr_row_index,
first_column_in_full_grid=first_narr_column_index,
plot_every_k_rows=PLOT_EVERY_KTH_WIND_BARB,
plot_every_k_columns=PLOT_EVERY_KTH_WIND_BARB,
barb_length=WIND_BARB_LENGTH,
empty_barb_radius=EMPTY_WIND_BARB_RADIUS,
colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT,
colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT)
num_fronts = len(front_line_table.index)
for i in range(num_fronts):
this_front_type_string = front_line_table[
front_utils.FRONT_TYPE_COLUMN].values[i]
if this_front_type_string == front_utils.WARM_FRONT_STRING_ID:
this_colour = WARM_FRONT_COLOUR
else:
this_colour = COLD_FRONT_COLOUR
# front_plotting.plot_polyline(
# latitudes_deg=front_line_table[
# front_utils.LATITUDES_COLUMN].values[i],
# longitudes_deg=front_line_table[
# front_utils.LONGITUDES_COLUMN].values[i],
# basemap_object=basemap_object, axes_object=axes_object,
# front_type=front_line_table[
# front_utils.FRONT_TYPE_COLUMN].values[i],
# line_width=FRONT_LINE_WIDTH, line_colour=this_colour)
print 'Saving figure to: "{0:s}"...'.format(OUTPUT_FILE_NAME)
file_system_utils.mkdir_recursive_if_necessary(file_name=OUTPUT_FILE_NAME)
pyplot.savefig(OUTPUT_FILE_NAME, dpi=OUTPUT_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=OUTPUT_FILE_NAME,
output_file_name=OUTPUT_FILE_NAME)