def latlng_limits_to_rowcol_limits(min_latitude_deg,
max_latitude_deg,
min_longitude_deg,
max_longitude_deg,
model_name,
grid_id=None):
"""Converts lat-long limits to row-column limits in the given model grid.
:param min_latitude_deg: Minimum latitude (deg N).
:param max_latitude_deg: Max latitude (deg N).
:param min_longitude_deg: Minimum longitude (deg E).
:param max_longitude_deg: Max longitude (deg E).
:param model_name: Name of NWP model (must be accepted by
`nwp_model_utils.check_grid_name`).
:param grid_id: Grid for NWP model (must be accepted by
`nwp_model_utils.check_grid_name`).
:return: row_limits: length-2 numpy array, containing min and max rows in
model grid, respectively.
:return: column_limits: Same but for columns.
"""
error_checking.assert_is_valid_latitude(min_latitude_deg)
error_checking.assert_is_valid_latitude(max_latitude_deg)
error_checking.assert_is_greater(max_latitude_deg, min_latitude_deg)
both_longitudes_deg = numpy.array([min_longitude_deg, max_longitude_deg])
both_longitudes_deg = lng_conversion.convert_lng_positive_in_west(
both_longitudes_deg)
min_longitude_deg = both_longitudes_deg[0]
max_longitude_deg = both_longitudes_deg[1]
error_checking.assert_is_greater(max_longitude_deg, min_longitude_deg)
grid_point_lat_matrix_deg, grid_point_lng_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(model_name=model_name,
grid_name=grid_id))
good_lat_flag_matrix = numpy.logical_and(
grid_point_lat_matrix_deg >= min_latitude_deg,
grid_point_lat_matrix_deg <= max_latitude_deg)
good_lng_flag_matrix = numpy.logical_and(
grid_point_lng_matrix_deg >= min_longitude_deg,
grid_point_lng_matrix_deg <= max_longitude_deg)
good_row_indices, good_column_indices = numpy.where(
numpy.logical_and(good_lat_flag_matrix, good_lng_flag_matrix))
row_limits = numpy.array(
[numpy.min(good_row_indices),
numpy.max(good_row_indices)], dtype=int)
column_limits = numpy.array(
[numpy.min(good_column_indices),
numpy.max(good_column_indices)],
dtype=int)
return row_limits, column_limits
def test_wind_rotation_angles_grid130(self):
"""Ensures approx correctness of wind-rotation angles on NCEP 130 grid.
This method ensures that the wind-rotation angle for all grid points can
be generated accurately from lat-long coordinates. Specifically, the
mean sine and cosine error must be <= 10^-5 and max error must be
<= 10^-4.
NOTE: This test relies on methods other than get_wind_rotation_angles,
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)
expected_cos_vector, expected_sin_vector = numpy.loadtxt(
GRID130_WIND_ROTATION_FILE_NAME, unpack=True)
expected_cos_matrix = numpy.reshape(
expected_cos_vector, (num_grid_rows, num_grid_columns))
expected_sin_matrix = numpy.reshape(
expected_sin_vector, (num_grid_rows, num_grid_columns))
grid_point_lat_matrix_deg, grid_point_lng_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.RAP_MODEL_NAME,
grid_id=nwp_model_utils.ID_FOR_130GRID))
rotation_angle_cos_matrix, rotation_angle_sin_matrix = (
nwp_model_utils.get_wind_rotation_angles(
grid_point_lat_matrix_deg, grid_point_lng_matrix_deg,
model_name=nwp_model_utils.RAP_MODEL_NAME))
cos_error_matrix = numpy.absolute(
rotation_angle_cos_matrix - expected_cos_matrix)
sin_error_matrix = numpy.absolute(
rotation_angle_sin_matrix - expected_sin_matrix)
self.assertTrue(
numpy.mean(cos_error_matrix) <= MAX_MEAN_SIN_OR_COS_ERROR)
self.assertTrue(numpy.max(cos_error_matrix) <= MAX_MAX_SIN_OR_COS_ERROR)
self.assertTrue(
numpy.mean(sin_error_matrix) <= MAX_MEAN_SIN_OR_COS_ERROR)
self.assertTrue(numpy.max(sin_error_matrix) <= MAX_MAX_SIN_OR_COS_ERROR)
def test_wind_rotation_angles_grid252(self):
"""Ensures approx correctness of rotation angles for NCEP 252 grid."""
num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions(
model_name=nwp_model_utils.RAP_MODEL_NAME,
grid_name=nwp_model_utils.NAME_OF_252GRID)
expected_cos_vector, expected_sin_vector = numpy.loadtxt(
GRID252_WIND_ROTATION_FILE_NAME, unpack=True)
expected_cos_matrix = numpy.reshape(expected_cos_vector,
(num_grid_rows, num_grid_columns))
expected_sin_matrix = numpy.reshape(expected_sin_vector,
(num_grid_rows, num_grid_columns))
latitude_matrix_deg, longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.RAP_MODEL_NAME,
grid_name=nwp_model_utils.NAME_OF_252GRID))
rotation_angle_cos_matrix, rotation_angle_sin_matrix = (
nwp_model_utils.get_wind_rotation_angles(
latitudes_deg=latitude_matrix_deg,
longitudes_deg=longitude_matrix_deg,
model_name=nwp_model_utils.RAP_MODEL_NAME))
cos_error_matrix = numpy.absolute(rotation_angle_cos_matrix -
expected_cos_matrix)
sin_error_matrix = numpy.absolute(rotation_angle_sin_matrix -
expected_sin_matrix)
self.assertTrue(
numpy.mean(cos_error_matrix) <= MAX_MEAN_SIN_OR_COS_ERROR)
self.assertTrue(
numpy.max(cos_error_matrix) <= MAX_MAX_SIN_OR_COS_ERROR)
self.assertTrue(
numpy.mean(sin_error_matrix) <= MAX_MEAN_SIN_OR_COS_ERROR)
self.assertTrue(
numpy.max(sin_error_matrix) <= MAX_MAX_SIN_OR_COS_ERROR)
def _get_grid_point_coords(model_name,
first_row_in_full_grid,
last_row_in_full_grid,
first_column_in_full_grid,
last_column_in_full_grid,
grid_id=None,
basemap_object=None):
"""Returns x-y and lat-long coords for a subgrid of the full model grid.
This method generates different x-y coordinates than
`nwp_model_utils.get_xy_grid_point_matrices`, because (like
`mpl_toolkits.basemap.Basemap`) this method sets false easting = false
northing = 0 metres.
:param model_name: Name of NWP model (must be accepted by
`nwp_model_utils.check_grid_name`).
:param first_row_in_full_grid: Row 0 in the subgrid is row
`first_row_in_full_grid` in the full grid.
:param last_row_in_full_grid: Last row in the subgrid is row
`last_row_in_full_grid` in the full grid. If you want last row in the
subgrid to equal last row in the full grid, make this -1.
:param first_column_in_full_grid: Column 0 in the subgrid is column
`first_column_in_full_grid` in the full grid.
:param last_column_in_full_grid: Last column in the subgrid is column
`last_column_in_full_grid` in the full grid. If you want last column in
the subgrid to equal last column in the full grid, make this -1.
:param grid_id: Grid for NWP model (must be accepted by
`nwp_model_utils.check_grid_name`).
:param basemap_object: Instance of `mpl_toolkits.basemap.Basemap` for the
given NWP model. If you don't have one, no big deal -- leave this
argument empty.
:return: coordinate_dict: Dictionary with the following keys.
coordinate_dict['grid_point_x_matrix_metres']: M-by-N numpy array of
x-coordinates.
coordinate_dict['grid_point_y_matrix_metres']: M-by-N numpy array of
y-coordinates.
coordinate_dict['grid_point_lat_matrix_deg']: M-by-N numpy array of
latitudes (deg N).
coordinate_dict['grid_point_lng_matrix_deg']: M-by-N numpy array of
longitudes (deg E).
"""
num_rows_in_full_grid, num_columns_in_full_grid = (
nwp_model_utils.get_grid_dimensions(model_name=model_name,
grid_name=grid_id))
error_checking.assert_is_integer(first_row_in_full_grid)
error_checking.assert_is_geq(first_row_in_full_grid, 0)
error_checking.assert_is_integer(last_row_in_full_grid)
if last_row_in_full_grid < 0:
last_row_in_full_grid += num_rows_in_full_grid
error_checking.assert_is_greater(last_row_in_full_grid,
first_row_in_full_grid)
error_checking.assert_is_less_than(last_row_in_full_grid,
num_rows_in_full_grid)
error_checking.assert_is_integer(first_column_in_full_grid)
error_checking.assert_is_geq(first_column_in_full_grid, 0)
error_checking.assert_is_integer(last_column_in_full_grid)
if last_column_in_full_grid < 0:
last_column_in_full_grid += num_columns_in_full_grid
error_checking.assert_is_greater(last_column_in_full_grid,
first_column_in_full_grid)
error_checking.assert_is_less_than(last_column_in_full_grid,
num_columns_in_full_grid)
grid_point_lat_matrix_deg, grid_point_lng_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(model_name=model_name,
grid_name=grid_id))
grid_point_lat_matrix_deg = grid_point_lat_matrix_deg[
first_row_in_full_grid:(last_row_in_full_grid + 1),
first_column_in_full_grid:(last_column_in_full_grid + 1)]
grid_point_lng_matrix_deg = grid_point_lng_matrix_deg[
first_row_in_full_grid:(last_row_in_full_grid + 1),
first_column_in_full_grid:(last_column_in_full_grid + 1)]
if basemap_object is None:
standard_latitudes_deg, central_longitude_deg = (
nwp_model_utils.get_projection_params(model_name))
projection_object = projections.init_lcc_projection(
standard_latitudes_deg=standard_latitudes_deg,
central_longitude_deg=central_longitude_deg)
grid_point_x_matrix_metres, grid_point_y_matrix_metres = (
projections.project_latlng_to_xy(
latitudes_deg=grid_point_lat_matrix_deg,
longitudes_deg=grid_point_lng_matrix_deg,
projection_object=projection_object,
false_northing_metres=0.,
false_easting_metres=0.))
else:
grid_point_x_matrix_metres, grid_point_y_matrix_metres = basemap_object(
grid_point_lng_matrix_deg, grid_point_lat_matrix_deg)
return {
X_COORD_MATRIX_KEY: grid_point_x_matrix_metres,
Y_COORD_MATRIX_KEY: grid_point_y_matrix_metres,
LATITUDE_MATRIX_KEY: grid_point_lat_matrix_deg,
LONGITUDE_MATRIX_KEY: grid_point_lng_matrix_deg,
}
FOURTH_LONGITUDES_DEG = numpy.array([246., 246.5])
THIS_DICT = {
front_utils.LATITUDES_COLUMN:
[FIRST_LATITUDES_DEG, SECOND_LATITUDES_DEG, THIRD_LATITUDES_DEG,
FOURTH_LATITUDES_DEG],
front_utils.LONGITUDES_COLUMN:
[FIRST_LONGITUDES_DEG, SECOND_LONGITUDES_DEG, THIRD_LONGITUDES_DEG,
FOURTH_LONGITUDES_DEG],
front_utils.TIME_COLUMN: THESE_TIMES_UNIX_SEC,
front_utils.FRONT_TYPE_COLUMN: THESE_STRINGS
}
POLYLINE_TABLE_BEFORE_MASK = pandas.DataFrame.from_dict(THIS_DICT)
(THIS_LATITUDE_MATRIX_DEG, THIS_LONGITUDE_MATRIX_DEG
) = nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME)
THIS_LATITUDE_FLAG_MATRIX = numpy.logical_and(
THIS_LATITUDE_MATRIX_DEG >= 30., THIS_LATITUDE_MATRIX_DEG <= 75.)
THIS_LONGITUDE_FLAG_MATRIX = numpy.logical_and(
THIS_LONGITUDE_MATRIX_DEG >= 220., THIS_LONGITUDE_MATRIX_DEG <= 320.)
NARR_MASK_MATRIX = numpy.logical_and(
THIS_LATITUDE_FLAG_MATRIX, THIS_LONGITUDE_FLAG_MATRIX
).astype(int)
THIS_DICT = {
front_utils.LATITUDES_COLUMN:
[FIRST_LATITUDES_DEG, SECOND_LATITUDES_DEG, THIRD_LATITUDES_DEG],
front_utils.LONGITUDES_COLUMN:
[FIRST_LONGITUDES_DEG, SECOND_LONGITUDES_DEG, THIRD_LONGITUDES_DEG],
front_utils.TIME_COLUMN: THESE_TIMES_UNIX_SEC[:-1],
def _run(top_narr_dir_name, top_front_line_dir_name, top_wpc_bulletin_dir_name,
first_time_string, last_time_string, pressure_level_mb,
thermal_field_name, thermal_colour_map_name,
max_thermal_prctile_for_colours, first_letter_label, letter_interval,
output_dir_name):
"""Plots predictors on full NARR grid.
This is effectively the main method.
:param top_narr_dir_name: See documentation at top of file.
:param top_front_line_dir_name: Same.
:param top_wpc_bulletin_dir_name: Same.
:param first_time_string: Same.
:param last_time_string: Same.
:param pressure_level_mb: Same.
:param thermal_field_name: Same.
:param thermal_colour_map_name: Same.
:param max_thermal_prctile_for_colours: Same.
:param first_letter_label: Same.
:param letter_interval: Same.
:param output_dir_name: Same.
:raises: ValueError: if
`thermal_field_name not in VALID_THERMAL_FIELD_NAMES`.
"""
# Check input args.
if top_wpc_bulletin_dir_name in ['', 'None']:
top_wpc_bulletin_dir_name = None
if first_letter_label in ['', 'None']:
first_letter_label = None
if thermal_field_name not in VALID_THERMAL_FIELD_NAMES:
error_string = (
'\n{0:s}\nValid thermal fields (listed above) do not include '
'"{1:s}".'
).format(str(VALID_THERMAL_FIELD_NAMES), thermal_field_name)
raise ValueError(error_string)
thermal_colour_map_object = pyplot.cm.get_cmap(thermal_colour_map_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
first_time_unix_sec = time_conversion.string_to_unix_sec(
first_time_string, DEFAULT_TIME_FORMAT)
last_time_unix_sec = time_conversion.string_to_unix_sec(
last_time_string, DEFAULT_TIME_FORMAT)
valid_times_unix_sec = time_periods.range_and_interval_to_list(
start_time_unix_sec=first_time_unix_sec,
end_time_unix_sec=last_time_unix_sec,
time_interval_sec=NARR_TIME_INTERVAL_SEC, include_endpoint=True)
# Read metadata for NARR grid.
narr_latitude_matrix_deg, narr_longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME)
)
narr_rotation_cos_matrix, narr_rotation_sin_matrix = (
nwp_model_utils.get_wind_rotation_angles(
latitudes_deg=narr_latitude_matrix_deg,
longitudes_deg=narr_longitude_matrix_deg,
model_name=nwp_model_utils.NARR_MODEL_NAME)
)
narr_row_limits, narr_column_limits = (
nwp_plotting.latlng_limits_to_rowcol_limits(
min_latitude_deg=MIN_LATITUDE_DEG,
max_latitude_deg=MAX_LATITUDE_DEG,
min_longitude_deg=MIN_LONGITUDE_DEG,
max_longitude_deg=MAX_LONGITUDE_DEG,
model_name=nwp_model_utils.NARR_MODEL_NAME)
)
narr_rotation_cos_matrix = narr_rotation_cos_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
narr_rotation_sin_matrix = narr_rotation_sin_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
# Do plotting.
narr_field_names = [
processed_narr_io.U_WIND_GRID_RELATIVE_NAME,
processed_narr_io.V_WIND_GRID_RELATIVE_NAME,
thermal_field_name
]
this_letter_label = None
for this_time_unix_sec in valid_times_unix_sec:
this_file_name = fronts_io.find_file_for_one_time(
top_directory_name=top_front_line_dir_name,
file_type=fronts_io.POLYLINE_FILE_TYPE,
valid_time_unix_sec=this_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_polyline_table = fronts_io.read_polylines_from_file(this_file_name)
if top_wpc_bulletin_dir_name is None:
this_high_low_table = None
else:
this_file_name = wpc_bulletin_io.find_file(
top_directory_name=top_wpc_bulletin_dir_name,
valid_time_unix_sec=this_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_high_low_table = wpc_bulletin_io.read_highs_and_lows(
this_file_name)
this_predictor_matrix = None
for this_field_name in narr_field_names:
this_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=top_narr_dir_name,
field_name=this_field_name,
pressure_level_mb=pressure_level_mb,
valid_time_unix_sec=this_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
this_field_matrix = processed_narr_io.read_fields_from_file(
this_file_name
)[0][0, ...]
this_field_matrix = utils.fill_nans(this_field_matrix)
this_field_matrix = this_field_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
if this_field_name in [processed_narr_io.TEMPERATURE_NAME,
processed_narr_io.WET_BULB_THETA_NAME]:
this_field_matrix -= ZERO_CELSIUS_IN_KELVINS
if this_field_name == processed_narr_io.SPECIFIC_HUMIDITY_NAME:
this_field_matrix = this_field_matrix * KG_TO_GRAMS
this_field_matrix = numpy.expand_dims(this_field_matrix, axis=-1)
if this_predictor_matrix is None:
this_predictor_matrix = this_field_matrix + 0.
else:
this_predictor_matrix = numpy.concatenate(
(this_predictor_matrix, this_field_matrix), axis=-1)
u_wind_index = narr_field_names.index(
processed_narr_io.U_WIND_GRID_RELATIVE_NAME)
v_wind_index = narr_field_names.index(
processed_narr_io.V_WIND_GRID_RELATIVE_NAME)
(this_predictor_matrix[..., u_wind_index],
this_predictor_matrix[..., v_wind_index]
) = nwp_model_utils.rotate_winds_to_earth_relative(
u_winds_grid_relative_m_s01=this_predictor_matrix[
..., u_wind_index],
v_winds_grid_relative_m_s01=this_predictor_matrix[
..., v_wind_index],
rotation_angle_cosines=narr_rotation_cos_matrix,
rotation_angle_sines=narr_rotation_sin_matrix)
this_title_string = time_conversion.unix_sec_to_string(
this_time_unix_sec, NICE_TIME_FORMAT)
if pressure_level_mb == 1013:
this_title_string += ' at surface'
else:
this_title_string += ' at {0:d} mb'.format(pressure_level_mb)
this_default_time_string = time_conversion.unix_sec_to_string(
this_time_unix_sec, DEFAULT_TIME_FORMAT)
this_output_file_name = '{0:s}/predictors_{1:s}.jpg'.format(
output_dir_name, this_default_time_string)
if first_letter_label is not None:
if this_letter_label is None:
this_letter_label = first_letter_label
else:
this_letter_label = chr(
ord(this_letter_label) + letter_interval
)
_plot_one_time(
predictor_matrix=this_predictor_matrix,
predictor_names=narr_field_names,
front_polyline_table=this_polyline_table,
high_low_table=this_high_low_table,
thermal_colour_map_object=thermal_colour_map_object,
max_thermal_prctile_for_colours=max_thermal_prctile_for_colours,
narr_row_limits=narr_row_limits,
narr_column_limits=narr_column_limits,
title_string=this_title_string, letter_label=this_letter_label,
output_file_name=this_output_file_name)
print '\n'
def _run(top_input_dir_name, first_time_string, last_time_string,
input_field_name, pressure_level_mb, top_output_dir_name):
"""Converts NARR data to a more convenient file format.
This is effectively the main method.
:param top_input_dir_name: See documentation at top of file.
:param first_time_string: Same.
:param last_time_string: Same.
:param input_field_name: Same.
:param pressure_level_mb: Same.
:param top_output_dir_name: Same.
"""
if pressure_level_mb <= 0:
pressure_level_mb = None
if pressure_level_mb is None:
output_pressure_level_mb = DUMMY_PRESSURE_LEVEL_MB + 0
else:
output_pressure_level_mb = pressure_level_mb + 0
first_time_unix_sec = time_conversion.string_to_unix_sec(
first_time_string, INPUT_TIME_FORMAT)
last_time_unix_sec = time_conversion.string_to_unix_sec(
last_time_string, INPUT_TIME_FORMAT)
valid_times_unix_sec = time_periods.range_and_interval_to_list(
start_time_unix_sec=first_time_unix_sec,
end_time_unix_sec=last_time_unix_sec,
time_interval_sec=TIME_INTERVAL_SECONDS)
if input_field_name == processed_narr_io.U_WIND_EARTH_RELATIVE_NAME:
input_field_name_other = (processed_narr_io.V_WIND_EARTH_RELATIVE_NAME)
elif input_field_name == processed_narr_io.V_WIND_EARTH_RELATIVE_NAME:
input_field_name_other = (processed_narr_io.U_WIND_EARTH_RELATIVE_NAME)
else:
input_field_name_other = None
input_field_name_grib1 = _std_to_grib1_field_name(
field_name=input_field_name, pressure_level_mb=pressure_level_mb)
if input_field_name in WIND_FIELD_NAMES:
input_field_name_other_grib1 = _std_to_grib1_field_name(
field_name=input_field_name_other,
pressure_level_mb=pressure_level_mb)
output_field_name = processed_narr_io.field_name_to_grid_relative(
input_field_name)
output_field_name_other = (
processed_narr_io.field_name_to_grid_relative(
input_field_name_other))
(narr_latitude_matrix_deg, narr_longitude_matrix_deg
) = nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME)
(narr_rotation_cosine_matrix,
narr_rotation_sine_matrix) = nwp_model_utils.get_wind_rotation_angles(
latitudes_deg=narr_latitude_matrix_deg,
longitudes_deg=narr_longitude_matrix_deg,
model_name=nwp_model_utils.NARR_MODEL_NAME)
else:
input_field_name_other_grib1 = None
output_field_name = input_field_name + ''
output_field_name_other = None
num_times = len(valid_times_unix_sec)
for i in range(num_times):
if input_field_name in WIND_FIELD_NAMES:
this_field_matrix_other = None
if valid_times_unix_sec[i] > LAST_GRIB_TIME_UNIX_SEC:
this_month_string = time_conversion.unix_sec_to_string(
valid_times_unix_sec[i], MONTH_TIME_FORMAT)
this_netcdf_file_name = narr_netcdf_io.find_file(
top_directory_name=top_input_dir_name,
field_name=input_field_name,
month_string=this_month_string,
is_surface=pressure_level_mb is None)
print 'Reading data from: "{0:s}"...'.format(this_netcdf_file_name)
this_field_matrix = narr_netcdf_io.read_file(
netcdf_file_name=this_netcdf_file_name,
field_name=input_field_name,
valid_time_unix_sec=valid_times_unix_sec[i],
pressure_level_mb=pressure_level_mb)
if input_field_name in WIND_FIELD_NAMES:
this_netcdf_file_name_other = narr_netcdf_io.find_file(
top_directory_name=top_input_dir_name,
field_name=input_field_name_other,
month_string=this_month_string,
is_surface=pressure_level_mb is None)
print 'Reading data from: "{0:s}"...'.format(
this_netcdf_file_name_other)
this_field_matrix_other = narr_netcdf_io.read_file(
netcdf_file_name=this_netcdf_file_name_other,
field_name=input_field_name_other,
valid_time_unix_sec=valid_times_unix_sec[i],
pressure_level_mb=pressure_level_mb)
else:
this_grib_file_name = nwp_model_io.find_grib_file(
top_directory_name=top_input_dir_name,
model_name=nwp_model_utils.NARR_MODEL_NAME,
init_time_unix_sec=valid_times_unix_sec[i],
lead_time_hours=0)
print 'Reading data from: "{0:s}"...'.format(this_grib_file_name)
this_field_matrix = nwp_model_io.read_field_from_grib_file(
grib_file_name=this_grib_file_name,
field_name_grib1=input_field_name_grib1,
model_name=nwp_model_utils.NARR_MODEL_NAME,
wgrib_exe_name=WGRIB_EXE_NAME,
wgrib2_exe_name=WGRIB2_EXE_NAME)
if input_field_name in WIND_FIELD_NAMES:
this_field_matrix_other = (
nwp_model_io.read_field_from_grib_file(
grib_file_name=this_grib_file_name,
field_name_grib1=input_field_name_other_grib1,
model_name=nwp_model_utils.NARR_MODEL_NAME,
wgrib_exe_name=WGRIB_EXE_NAME,
wgrib2_exe_name=WGRIB2_EXE_NAME))
if input_field_name in WIND_FIELD_NAMES:
print 'Rotating Earth-relative winds to grid-relative...'
if input_field_name == processed_narr_io.U_WIND_EARTH_RELATIVE_NAME:
this_field_matrix, this_field_matrix_other = (
nwp_model_utils.rotate_winds_to_grid_relative(
u_winds_earth_relative_m_s01=this_field_matrix,
v_winds_earth_relative_m_s01=this_field_matrix_other,
rotation_angle_cosines=narr_rotation_cosine_matrix,
rotation_angle_sines=narr_rotation_sine_matrix))
else:
this_field_matrix_other, this_field_matrix = (
nwp_model_utils.rotate_winds_to_grid_relative(
u_winds_earth_relative_m_s01=this_field_matrix_other,
v_winds_earth_relative_m_s01=this_field_matrix,
rotation_angle_cosines=narr_rotation_cosine_matrix,
rotation_angle_sines=narr_rotation_sine_matrix))
this_output_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=top_output_dir_name,
field_name=output_field_name,
pressure_level_mb=output_pressure_level_mb,
valid_time_unix_sec=valid_times_unix_sec[i],
raise_error_if_missing=False)
print 'Writing processed data to: "{0:s}"...'.format(
this_output_file_name)
processed_narr_io.write_fields_to_file(
pickle_file_name=this_output_file_name,
field_matrix=numpy.expand_dims(this_field_matrix, axis=0),
field_name=output_field_name,
pressure_level_pascals=output_pressure_level_mb * MB_TO_PASCALS,
valid_times_unix_sec=valid_times_unix_sec[[i]])
if input_field_name not in WIND_FIELD_NAMES:
print '\n'
continue
this_output_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=top_output_dir_name,
field_name=output_field_name_other,
pressure_level_mb=output_pressure_level_mb,
valid_time_unix_sec=valid_times_unix_sec[i],
raise_error_if_missing=False)
print 'Writing processed data to: "{0:s}"...\n'.format(
this_output_file_name)
processed_narr_io.write_fields_to_file(
pickle_file_name=this_output_file_name,
field_matrix=numpy.expand_dims(this_field_matrix_other, axis=0),
field_name=output_field_name_other,
pressure_level_pascals=output_pressure_level_mb * MB_TO_PASCALS,
valid_times_unix_sec=valid_times_unix_sec[[i]])
def _run():
"""Plots weird WPC fronts, along with theta_w and wind barbs from NARR.
This is effectively the main method.
"""
narr_latitude_matrix_deg, narr_longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME))
narr_rotation_cos_matrix, narr_rotation_sin_matrix = (
nwp_model_utils.get_wind_rotation_angles(
latitudes_deg=narr_latitude_matrix_deg,
longitudes_deg=narr_longitude_matrix_deg,
model_name=nwp_model_utils.NARR_MODEL_NAME))
num_panels = len(VALID_TIME_STRINGS)
panel_file_names = [''] * num_panels
this_annotation_string = None
for i in range(num_panels):
this_time_unix_sec = time_conversion.string_to_unix_sec(
VALID_TIME_STRINGS[i], DEFAULT_TIME_FORMAT)
this_title_string = time_conversion.unix_sec_to_string(
this_time_unix_sec, NICE_TIME_FORMAT)
if PRESSURE_LEVELS_MB[i] == 1000:
this_title_string += ' at 1000 mb'
else:
this_title_string += ' at surface'
if this_annotation_string is None:
this_annotation_string = '(a)'
else:
this_orig_character = this_annotation_string[1]
this_new_character = chr(ord(this_orig_character) + 1)
this_annotation_string = this_annotation_string.replace(
this_orig_character, this_new_character)
this_file_name = _plot_one_time(
valid_time_string=VALID_TIME_STRINGS[i],
pressure_level_mb=PRESSURE_LEVELS_MB[i],
title_string=this_title_string,
annotation_string=this_annotation_string,
narr_rotation_cos_matrix=narr_rotation_cos_matrix,
narr_rotation_sin_matrix=narr_rotation_sin_matrix)
panel_file_names[i] = this_file_name
print '\n'
concat_file_name = '{0:s}/weird_fronts.jpg'.format(OUTPUT_DIR_NAME)
print 'Concatenating figures to: "{0:s}"...'.format(concat_file_name)
num_panels = len(panel_file_names)
num_panel_columns = int(numpy.ceil(float(num_panels) / 2))
imagemagick_utils.concatenate_images(input_file_names=panel_file_names,
output_file_name=concat_file_name,
num_panel_rows=NUM_PANEL_ROWS,
num_panel_columns=num_panel_columns)
imagemagick_utils.trim_whitespace(input_file_name=concat_file_name,
output_file_name=concat_file_name)
imagemagick_utils.resize_image(input_file_name=concat_file_name,
output_file_name=concat_file_name,
output_size_pixels=CONCAT_SIZE_PIXELS)
def get_xy_grid_point_matrices(first_row_in_narr_grid,
last_row_in_narr_grid,
first_column_in_narr_grid,
last_column_in_narr_grid,
basemap_object=None):
"""Returns coordinate matrices for a contiguous subset of the NARR grid.
However, this subset need not be *strictly* a subset. In other words, the
"subset" could be the full NARR grid.
This method generates different x- and y-coordinates than
`nwp_model_utils.get_xy_grid_point_matrices`, because (like
`mpl_toolkits.basemap.Basemap`) this method assumes that false easting and
northing are zero.
:param first_row_in_narr_grid: Row 0 in the subgrid is row
`first_row_in_narr_grid` in the full NARR grid.
:param last_row_in_narr_grid: Last row (index -1) in the subgrid is row
`last_row_in_narr_grid` in the full NARR grid.
:param first_column_in_narr_grid: Column 0 in the subgrid is row
`first_column_in_narr_grid` in the full NARR grid.
:param last_column_in_narr_grid: Last column (index -1) in the subgrid is
row `last_column_in_narr_grid` in the full NARR grid.
:param basemap_object: Instance of `mpl_toolkits.basemap.Basemap` created
for the NARR grid. If you don't have one, no big deal -- leave this
argument empty.
:return: grid_point_x_matrix_metres: M-by-N numpy array of x-coordinates.
:return: grid_point_y_matrix_metres: M-by-N numpy array of y-coordinates.
"""
error_checking.assert_is_integer(first_row_in_narr_grid)
error_checking.assert_is_geq(first_row_in_narr_grid, 0)
error_checking.assert_is_integer(last_row_in_narr_grid)
error_checking.assert_is_greater(last_row_in_narr_grid,
first_row_in_narr_grid)
error_checking.assert_is_less_than(last_row_in_narr_grid,
NUM_ROWS_IN_NARR_GRID)
error_checking.assert_is_integer(first_column_in_narr_grid)
error_checking.assert_is_geq(first_column_in_narr_grid, 0)
error_checking.assert_is_integer(last_column_in_narr_grid)
error_checking.assert_is_greater(last_column_in_narr_grid,
first_column_in_narr_grid)
error_checking.assert_is_less_than(last_column_in_narr_grid,
NUM_COLUMNS_IN_NARR_GRID)
latitude_matrix_deg, longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME))
latitude_matrix_deg = latitude_matrix_deg[first_row_in_narr_grid:(
last_row_in_narr_grid +
1), first_column_in_narr_grid:(last_column_in_narr_grid + 1)]
longitude_matrix_deg = longitude_matrix_deg[first_row_in_narr_grid:(
last_row_in_narr_grid +
1), first_column_in_narr_grid:(last_column_in_narr_grid + 1)]
if basemap_object is None:
standard_latitudes_deg, central_longitude_deg = (
nwp_model_utils.get_projection_params(
nwp_model_utils.NARR_MODEL_NAME))
projection_object = projections.init_lambert_conformal_projection(
standard_latitudes_deg=standard_latitudes_deg,
central_longitude_deg=central_longitude_deg)
grid_point_x_matrix_metres, grid_point_y_matrix_metres = (
projections.project_latlng_to_xy(
latitude_matrix_deg,
longitude_matrix_deg,
projection_object=projection_object,
false_northing_metres=0.,
false_easting_metres=0.))
else:
grid_point_x_matrix_metres, grid_point_y_matrix_metres = (
basemap_object(longitude_matrix_deg, latitude_matrix_deg))
return grid_point_x_matrix_metres, grid_point_y_matrix_metres
def _run(example_file_name, top_front_line_dir_name, num_examples,
example_indices, thetaw_colour_map_name, thetaw_max_colour_percentile,
output_dir_name):
"""Plots one or more input examples.
This is effectively the main method.
:param example_file_name: See documentation at top of file.
:param top_front_line_dir_name: Same.
:param num_examples: Same.
:param example_indices: Same.
:param thetaw_colour_map_name: Same.
:param thetaw_max_colour_percentile: Same.
:param output_dir_name: Same.
"""
if num_examples <= 0:
num_examples = None
if num_examples is None:
error_checking.assert_is_geq_numpy_array(example_indices, 0)
else:
error_checking.assert_is_greater(num_examples, 0)
error_checking.assert_is_geq(thetaw_max_colour_percentile, 0)
error_checking.assert_is_leq(thetaw_max_colour_percentile, 100)
thetaw_colour_map_object = pyplot.cm.get_cmap(thetaw_colour_map_name)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name)
print 'Reading normalized examples from: "{0:s}"...'.format(
example_file_name)
example_dict = trainval_io.read_downsized_3d_examples(
netcdf_file_name=example_file_name,
num_half_rows_to_keep=NUM_HALF_ROWS,
num_half_columns_to_keep=NUM_HALF_COLUMNS,
predictor_names_to_keep=NARR_PREDICTOR_NAMES)
# TODO(thunderhoser): This is a HACK (assuming that normalization method is
# z-score and not min-max).
mean_value_matrix = example_dict[trainval_io.FIRST_NORM_PARAM_KEY]
standard_deviation_matrix = example_dict[trainval_io.SECOND_NORM_PARAM_KEY]
normalization_dict = {
ml_utils.MIN_VALUE_MATRIX_KEY: None,
ml_utils.MAX_VALUE_MATRIX_KEY: None,
ml_utils.MEAN_VALUE_MATRIX_KEY: mean_value_matrix,
ml_utils.STDEV_MATRIX_KEY: standard_deviation_matrix
}
example_dict[trainval_io.PREDICTOR_MATRIX_KEY] = (
ml_utils.denormalize_predictors(
predictor_matrix=example_dict[trainval_io.PREDICTOR_MATRIX_KEY],
normalization_dict=normalization_dict))
narr_latitude_matrix_deg, narr_longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME))
narr_rotation_cos_matrix, narr_rotation_sin_matrix = (
nwp_model_utils.get_wind_rotation_angles(
latitudes_deg=narr_latitude_matrix_deg,
longitudes_deg=narr_longitude_matrix_deg,
model_name=nwp_model_utils.NARR_MODEL_NAME))
num_examples_total = len(example_dict[trainval_io.TARGET_TIMES_KEY])
example_indices = numpy.linspace(0,
num_examples_total - 1,
num=num_examples_total,
dtype=int)
if num_examples is not None:
num_examples = min([num_examples, num_examples_total])
example_indices = numpy.random.choice(example_indices,
size=num_examples,
replace=False)
thetaw_index = NARR_PREDICTOR_NAMES.index(
processed_narr_io.WET_BULB_THETA_NAME)
u_wind_index = NARR_PREDICTOR_NAMES.index(
processed_narr_io.U_WIND_GRID_RELATIVE_NAME)
v_wind_index = NARR_PREDICTOR_NAMES.index(
processed_narr_io.V_WIND_GRID_RELATIVE_NAME)
for i in example_indices:
this_center_row_index = example_dict[trainval_io.ROW_INDICES_KEY][i]
this_first_row_index = this_center_row_index - NUM_HALF_ROWS
this_last_row_index = this_center_row_index + NUM_HALF_ROWS
this_center_column_index = example_dict[
trainval_io.COLUMN_INDICES_KEY][i]
this_first_column_index = this_center_column_index - NUM_HALF_COLUMNS
this_last_column_index = this_center_column_index + NUM_HALF_COLUMNS
this_u_wind_matrix_m_s01 = example_dict[
trainval_io.PREDICTOR_MATRIX_KEY][i, ..., u_wind_index]
this_v_wind_matrix_m_s01 = example_dict[
trainval_io.PREDICTOR_MATRIX_KEY][i, ..., v_wind_index]
this_cos_matrix = narr_rotation_cos_matrix[this_first_row_index:(
this_last_row_index +
1), this_first_column_index:(this_last_column_index + 1)]
this_sin_matrix = narr_rotation_sin_matrix[this_first_row_index:(
this_last_row_index +
1), this_first_column_index:(this_last_column_index + 1)]
this_u_wind_matrix_m_s01, this_v_wind_matrix_m_s01 = (
nwp_model_utils.rotate_winds_to_earth_relative(
u_winds_grid_relative_m_s01=this_u_wind_matrix_m_s01,
v_winds_grid_relative_m_s01=this_v_wind_matrix_m_s01,
rotation_angle_cosines=this_cos_matrix,
rotation_angle_sines=this_sin_matrix))
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=this_first_row_index,
last_row_in_full_grid=this_last_row_index,
first_column_in_full_grid=this_first_column_index,
last_column_in_full_grid=this_last_column_index,
resolution_string='i')
plotting_utils.plot_coastlines(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR,
line_width=BORDER_WIDTH)
plotting_utils.plot_countries(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR,
line_width=BORDER_WIDTH)
plotting_utils.plot_states_and_provinces(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR,
line_width=BORDER_WIDTH)
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)
this_thetaw_matrix_kelvins = example_dict[
trainval_io.PREDICTOR_MATRIX_KEY][i, ..., thetaw_index]
this_min_value = numpy.percentile(this_thetaw_matrix_kelvins,
100. - thetaw_max_colour_percentile)
this_max_value = numpy.percentile(this_thetaw_matrix_kelvins,
thetaw_max_colour_percentile)
nwp_plotting.plot_subgrid(
field_matrix=this_thetaw_matrix_kelvins,
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=thetaw_colour_map_object,
min_value_in_colour_map=this_min_value,
max_value_in_colour_map=this_max_value,
first_row_in_full_grid=this_first_row_index,
first_column_in_full_grid=this_first_column_index)
colour_bar_object = plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=this_thetaw_matrix_kelvins,
colour_map=thetaw_colour_map_object,
colour_min=this_min_value,
colour_max=this_max_value,
orientation='vertical',
extend_min=True,
extend_max=True,
fraction_of_axis_length=0.8)
colour_bar_object.set_label(
r'Wet-bulb potential temperature ($^{\circ}$C)')
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=this_u_wind_matrix_m_s01,
v_wind_matrix_m_s01=this_v_wind_matrix_m_s01,
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_full_grid=this_first_row_index,
first_column_in_full_grid=this_first_column_index,
barb_length=WIND_BARB_LENGTH,
empty_barb_radius=EMPTY_WIND_BARB_RADIUS,
fill_empty_barb=False,
colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT,
colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT)
this_front_file_name = fronts_io.find_file_for_one_time(
top_directory_name=top_front_line_dir_name,
file_type=fronts_io.POLYLINE_FILE_TYPE,
valid_time_unix_sec=example_dict[trainval_io.TARGET_TIMES_KEY][i])
print time_conversion.unix_sec_to_string(
example_dict[trainval_io.TARGET_TIMES_KEY][i], '%Y-%m-%d-%H')
this_polyline_table = fronts_io.read_polylines_from_file(
this_front_file_name)
this_num_fronts = len(this_polyline_table.index)
for j in range(this_num_fronts):
this_front_type_string = this_polyline_table[
front_utils.FRONT_TYPE_COLUMN].values[j]
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_front_with_markers(
line_latitudes_deg=this_polyline_table[
front_utils.LATITUDES_COLUMN].values[j],
line_longitudes_deg=this_polyline_table[
front_utils.LONGITUDES_COLUMN].values[j],
axes_object=axes_object,
basemap_object=basemap_object,
front_type_string=this_polyline_table[
front_utils.FRONT_TYPE_COLUMN].values[j],
marker_colour=this_colour,
marker_size=FRONT_MARKER_SIZE,
marker_spacing_metres=FRONT_SPACING_METRES)
this_output_file_name = '{0:s}/example{1:06d}.jpg'.format(
output_dir_name, i)
print 'Saving figure to: "{0:s}"...'.format(this_output_file_name)
pyplot.savefig(this_output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
def _plot_rapruc_one_example(
full_storm_id_string, storm_time_unix_sec, top_tracking_dir_name,
latitude_buffer_deg, longitude_buffer_deg, lead_time_seconds,
field_name_grib1, output_dir_name, rap_file_name=None,
ruc_file_name=None):
"""Plots RAP or RUC field for one example.
:param full_storm_id_string: Full storm ID.
:param storm_time_unix_sec: Valid time.
:param top_tracking_dir_name: See documentation at top of file.
:param latitude_buffer_deg: Same.
:param longitude_buffer_deg: Same.
:param lead_time_seconds: Same.
:param field_name_grib1: Same.
:param output_dir_name: Same.
:param rap_file_name: Path to file with RAP analysis.
:param ruc_file_name: [used only if `rap_file_name is None`]
Path to file with RUC analysis.
"""
tracking_file_name = tracking_io.find_file(
top_tracking_dir_name=top_tracking_dir_name,
tracking_scale_metres2=DUMMY_TRACKING_SCALE_METRES2,
source_name=tracking_utils.SEGMOTION_NAME,
valid_time_unix_sec=storm_time_unix_sec,
spc_date_string=
time_conversion.time_to_spc_date_string(storm_time_unix_sec),
raise_error_if_missing=True
)
print('Reading data from: "{0:s}"...'.format(tracking_file_name))
storm_object_table = tracking_io.read_file(tracking_file_name)
storm_object_table = storm_object_table.loc[
storm_object_table[tracking_utils.FULL_ID_COLUMN] ==
full_storm_id_string
]
extrap_times_sec = numpy.array([0, lead_time_seconds], dtype=int)
storm_object_table = soundings._create_target_points_for_interp(
storm_object_table=storm_object_table,
lead_times_seconds=extrap_times_sec
)
orig_latitude_deg = (
storm_object_table[tracking_utils.CENTROID_LATITUDE_COLUMN].values[0]
)
orig_longitude_deg = (
storm_object_table[tracking_utils.CENTROID_LONGITUDE_COLUMN].values[0]
)
extrap_latitude_deg = (
storm_object_table[tracking_utils.CENTROID_LATITUDE_COLUMN].values[1]
)
extrap_longitude_deg = (
storm_object_table[tracking_utils.CENTROID_LONGITUDE_COLUMN].values[1]
)
if rap_file_name is None:
grib_file_name = ruc_file_name
model_name = nwp_model_utils.RUC_MODEL_NAME
else:
grib_file_name = rap_file_name
model_name = nwp_model_utils.RAP_MODEL_NAME
pathless_grib_file_name = os.path.split(grib_file_name)[-1]
grid_name = pathless_grib_file_name.split('_')[1]
host_name = socket.gethostname()
if 'casper' in host_name:
wgrib_exe_name = '/glade/work/ryanlage/wgrib/wgrib'
wgrib2_exe_name = '/glade/work/ryanlage/wgrib2/wgrib2/wgrib2'
else:
wgrib_exe_name = '/condo/swatwork/ralager/wgrib/wgrib'
wgrib2_exe_name = '/condo/swatwork/ralager/grib2/wgrib2/wgrib2'
print('Reading field "{0:s}" from: "{1:s}"...'.format(
field_name_grib1, grib_file_name
))
main_field_matrix = nwp_model_io.read_field_from_grib_file(
grib_file_name=grib_file_name, field_name_grib1=field_name_grib1,
model_name=model_name, grid_id=grid_name,
wgrib_exe_name=wgrib_exe_name, wgrib2_exe_name=wgrib2_exe_name
)
u_wind_name_grib1 = 'UGRD:{0:s}'.format(
field_name_grib1.split(':')[-1]
)
u_wind_name_grib1 = u_wind_name_grib1.replace('2 m', '10 m')
print('Reading field "{0:s}" from: "{1:s}"...'.format(
u_wind_name_grib1, grib_file_name
))
u_wind_matrix_m_s01 = nwp_model_io.read_field_from_grib_file(
grib_file_name=grib_file_name, field_name_grib1=u_wind_name_grib1,
model_name=model_name, grid_id=grid_name,
wgrib_exe_name=wgrib_exe_name, wgrib2_exe_name=wgrib2_exe_name
)
v_wind_name_grib1 = 'VGRD:{0:s}'.format(
u_wind_name_grib1.split(':')[-1]
)
print('Reading field "{0:s}" from: "{1:s}"...'.format(
v_wind_name_grib1, grib_file_name
))
v_wind_matrix_m_s01 = nwp_model_io.read_field_from_grib_file(
grib_file_name=grib_file_name, field_name_grib1=v_wind_name_grib1,
model_name=model_name, grid_id=grid_name,
wgrib_exe_name=wgrib_exe_name, wgrib2_exe_name=wgrib2_exe_name
)
latitude_matrix_deg, longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=model_name, grid_name=grid_name)
)
cosine_matrix, sine_matrix = nwp_model_utils.get_wind_rotation_angles(
latitudes_deg=latitude_matrix_deg, longitudes_deg=longitude_matrix_deg,
model_name=model_name
)
u_wind_matrix_m_s01, v_wind_matrix_m_s01 = (
nwp_model_utils.rotate_winds_to_earth_relative(
u_winds_grid_relative_m_s01=u_wind_matrix_m_s01,
v_winds_grid_relative_m_s01=v_wind_matrix_m_s01,
rotation_angle_cosines=cosine_matrix,
rotation_angle_sines=sine_matrix)
)
min_plot_latitude_deg = (
min([orig_latitude_deg, extrap_latitude_deg]) - latitude_buffer_deg
)
max_plot_latitude_deg = (
max([orig_latitude_deg, extrap_latitude_deg]) + latitude_buffer_deg
)
min_plot_longitude_deg = (
min([orig_longitude_deg, extrap_longitude_deg]) - longitude_buffer_deg
)
max_plot_longitude_deg = (
max([orig_longitude_deg, extrap_longitude_deg]) + longitude_buffer_deg
)
row_limits, column_limits = nwp_plotting.latlng_limits_to_rowcol_limits(
min_latitude_deg=min_plot_latitude_deg,
max_latitude_deg=max_plot_latitude_deg,
min_longitude_deg=min_plot_longitude_deg,
max_longitude_deg=max_plot_longitude_deg,
model_name=model_name, grid_id=grid_name
)
main_field_matrix = main_field_matrix[
row_limits[0]:(row_limits[1] + 1),
column_limits[0]:(column_limits[1] + 1)
]
u_wind_matrix_m_s01 = u_wind_matrix_m_s01[
row_limits[0]:(row_limits[1] + 1),
column_limits[0]:(column_limits[1] + 1)
]
v_wind_matrix_m_s01 = v_wind_matrix_m_s01[
row_limits[0]:(row_limits[1] + 1),
column_limits[0]:(column_limits[1] + 1)
]
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=model_name, grid_id=grid_name,
first_row_in_full_grid=row_limits[0],
last_row_in_full_grid=row_limits[1],
first_column_in_full_grid=column_limits[0],
last_column_in_full_grid=column_limits[1]
)
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,
num_parallels=NUM_PARALLELS
)
plotting_utils.plot_meridians(
basemap_object=basemap_object, axes_object=axes_object,
num_meridians=NUM_MERIDIANS
)
min_colour_value = numpy.nanpercentile(
main_field_matrix, 100. - MAX_COLOUR_PERCENTILE
)
max_colour_value = numpy.nanpercentile(
main_field_matrix, MAX_COLOUR_PERCENTILE
)
nwp_plotting.plot_subgrid(
field_matrix=main_field_matrix,
model_name=model_name, grid_id=grid_name,
axes_object=axes_object, basemap_object=basemap_object,
colour_map_object=COLOUR_MAP_OBJECT, min_colour_value=min_colour_value,
max_colour_value=max_colour_value,
first_row_in_full_grid=row_limits[0],
first_column_in_full_grid=column_limits[0]
)
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=u_wind_matrix_m_s01,
v_wind_matrix_m_s01=v_wind_matrix_m_s01,
model_name=model_name, grid_id=grid_name,
axes_object=axes_object, basemap_object=basemap_object,
first_row_in_full_grid=row_limits[0],
first_column_in_full_grid=column_limits[0],
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,
fill_empty_barb=True, colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_WIND_SPEED_KT, colour_maximum_kt=MAX_WIND_SPEED_KT
)
orig_x_metres, orig_y_metres = basemap_object(
orig_longitude_deg, orig_latitude_deg
)
axes_object.plot(
orig_x_metres, orig_y_metres, linestyle='None',
marker=ORIGIN_MARKER_TYPE, markersize=ORIGIN_MARKER_SIZE,
markeredgewidth=ORIGIN_MARKER_EDGE_WIDTH,
markerfacecolor=MARKER_COLOUR, markeredgecolor=MARKER_COLOUR
)
extrap_x_metres, extrap_y_metres = basemap_object(
extrap_longitude_deg, extrap_latitude_deg
)
axes_object.plot(
extrap_x_metres, extrap_y_metres, linestyle='None',
marker=EXTRAP_MARKER_TYPE, markersize=EXTRAP_MARKER_SIZE,
markeredgewidth=EXTRAP_MARKER_EDGE_WIDTH,
markerfacecolor=MARKER_COLOUR, markeredgecolor=MARKER_COLOUR
)
plotting_utils.plot_linear_colour_bar(
axes_object_or_matrix=axes_object, data_matrix=main_field_matrix,
colour_map_object=COLOUR_MAP_OBJECT,
min_value=min_colour_value, max_value=max_colour_value,
orientation_string='vertical'
)
output_file_name = '{0:s}/{1:s}_{2:s}.jpg'.format(
output_dir_name, full_storm_id_string.replace('_', '-'),
time_conversion.unix_sec_to_string(
storm_time_unix_sec, FILE_NAME_TIME_FORMAT
)
)
print('Saving figure to: "{0:s}"...'.format(output_file_name))
pyplot.savefig(
output_file_name, dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0, bbox_inches='tight'
)
pyplot.close()
def _plot_one_time(predicted_region_table,
title_string,
letter_label,
output_file_name,
class_probability_matrix=None,
predicted_label_matrix=None,
plot_wf_colour_bar=True,
plot_cf_colour_bar=True):
"""Plots predictions at one time.
Either `class_probability_matrix` or `predicted_label_matrix` will be
plotted -- not both.
M = number of rows in NARR grid
N = number of columns in NARR grid
:param predicted_region_table: Subset of pandas DataFrame returned by
`object_eval.read_predictions_and_obs`, containing predicted fronts at
only one time.
:param title_string: Title (will be placed above figure).
:param letter_label: Letter label. If this is "a", the label "(a)" will be
printed at the top left of the figure.
:param output_file_name: Path to output file.
:param class_probability_matrix: M-by-N-by-3 numpy array of class
probabilities.
:param predicted_label_matrix: M-by-N numpy array of predicted labels
(integers in `front_utils.VALID_INTEGER_IDS`).
:param plot_wf_colour_bar: Boolean flag. If True, will plot colour bar for
warm-front probability.
:param plot_cf_colour_bar: Boolean flag. If True, will plot colour bar for
cold-front probability.
"""
narr_row_limits, narr_column_limits = (
nwp_plotting.latlng_limits_to_rowcol_limits(
min_latitude_deg=MIN_LATITUDE_DEG,
max_latitude_deg=MAX_LATITUDE_DEG,
min_longitude_deg=MIN_LONGITUDE_DEG,
max_longitude_deg=MAX_LONGITUDE_DEG,
model_name=nwp_model_utils.NARR_MODEL_NAME))
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=narr_row_limits[0],
last_row_in_full_grid=narr_row_limits[1],
first_column_in_full_grid=narr_column_limits[0],
last_column_in_full_grid=narr_column_limits[1])
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)
if class_probability_matrix is None:
this_matrix = predicted_label_matrix[narr_row_limits[0]:(
narr_row_limits[1] +
1), narr_column_limits[0]:(narr_column_limits[1] + 1)]
front_plotting.plot_narr_grid(
frontal_grid_matrix=this_matrix,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_narr_grid=narr_row_limits[0],
first_column_in_narr_grid=narr_column_limits[0],
opacity=0.25)
else:
this_wf_probability_matrix = class_probability_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1),
front_utils.WARM_FRONT_INTEGER_ID]
this_wf_probability_matrix[numpy.isnan(
this_wf_probability_matrix)] = 0.
prediction_plotting.plot_narr_grid(
probability_matrix=this_wf_probability_matrix,
front_string_id=front_utils.WARM_FRONT_STRING_ID,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_narr_grid=narr_row_limits[0],
first_column_in_narr_grid=narr_column_limits[0],
opacity=0.5)
this_cf_probability_matrix = class_probability_matrix[
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1),
front_utils.COLD_FRONT_INTEGER_ID]
this_cf_probability_matrix[numpy.isnan(
this_cf_probability_matrix)] = 0.
prediction_plotting.plot_narr_grid(
probability_matrix=this_cf_probability_matrix,
front_string_id=front_utils.COLD_FRONT_STRING_ID,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_narr_grid=narr_row_limits[0],
first_column_in_narr_grid=narr_column_limits[0],
opacity=0.5)
if plot_wf_colour_bar:
this_colour_map_object, this_colour_norm_object = (
prediction_plotting.get_warm_front_colour_map()[:2])
plotting_utils.add_colour_bar(
axes_object_or_list=axes_object,
colour_map=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
values_to_colour=this_wf_probability_matrix,
orientation='horizontal',
extend_min=True,
extend_max=False,
fraction_of_axis_length=0.9)
if plot_cf_colour_bar:
this_colour_map_object, this_colour_norm_object = (
prediction_plotting.get_cold_front_colour_map()[:2])
plotting_utils.add_colour_bar(
axes_object_or_list=axes_object,
colour_map=this_colour_map_object,
colour_norm_object=this_colour_norm_object,
values_to_colour=this_cf_probability_matrix,
orientation='horizontal',
extend_min=True,
extend_max=False,
fraction_of_axis_length=0.9)
narr_latitude_matrix_deg, narr_longitude_matrix_deg = (
nwp_model_utils.get_latlng_grid_point_matrices(
model_name=nwp_model_utils.NARR_MODEL_NAME))
num_objects = len(predicted_region_table.index)
for i in range(num_objects):
these_rows = predicted_region_table[
object_eval.ROW_INDICES_COLUMN].values[i]
these_columns = predicted_region_table[
object_eval.COLUMN_INDICES_COLUMN].values[i]
front_plotting.plot_polyline(
latitudes_deg=narr_latitude_matrix_deg[these_rows, these_columns],
longitudes_deg=narr_longitude_matrix_deg[these_rows,
these_columns],
axes_object=axes_object,
basemap_object=basemap_object,
front_type=predicted_region_table[
front_utils.FRONT_TYPE_COLUMN].values[i],
line_width=4)
# predicted_object_matrix = object_eval.regions_to_images(
# predicted_region_table=predicted_region_table,
# num_grid_rows=num_grid_rows, num_grid_columns=num_grid_columns)
#
# this_matrix = predicted_object_matrix[
# 0,
# narr_row_limits[0]:(narr_row_limits[1] + 1),
# narr_column_limits[0]:(narr_column_limits[1] + 1)
# ]
#
# front_plotting.plot_narr_grid(
# frontal_grid_matrix=this_matrix, axes_object=axes_object,
# basemap_object=basemap_object,
# first_row_in_narr_grid=narr_row_limits[0],
# first_column_in_narr_grid=narr_column_limits[0], opacity=1.)
pyplot.title(title_string)
if letter_label is not None:
plotting_utils.annotate_axes(
axes_object=axes_object,
annotation_string='({0:s})'.format(letter_label))
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
