def plot_one_storm_cell_to_winds(
storm_to_winds_table, storm_id, basemap_object=None, axes_object=None,
storm_colour=storm_plotting.DEFAULT_TRACK_COLOUR,
storm_line_width=storm_plotting.DEFAULT_TRACK_WIDTH,
wind_barb_length=wind_plotting.DEFAULT_BARB_LENGTH,
empty_wind_barb_radius=wind_plotting.DEFAULT_EMPTY_BARB_RADIUS,
fill_empty_wind_barb=wind_plotting.FILL_EMPTY_BARB_DEFAULT,
wind_colour_map=wind_plotting.DEFAULT_COLOUR_MAP,
colour_minimum_kt=wind_plotting.DEFAULT_COLOUR_MINIMUM_KT,
colour_maximum_kt=wind_plotting.DEFAULT_COLOUR_MAXIMUM_KT):
"""Plots wind observations linked to one storm cell.
:param storm_to_winds_table: pandas DataFrame with columns documented in
`link_storms_to_winds.write_storm_to_winds_table`.
:param storm_id: String ID for storm cell. Only this storm cell and wind
observations linked thereto will be plotted.
:param basemap_object: Instance of `mpl_toolkits.basemap.Basemap`.
:param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.
:param storm_colour: Colour for storm track, first storm object, and last
storm object (in any format accepted by `matplotlib.colors`).
:param storm_line_width: Line width for storm track, first storm object, and
last storm object (real positive number).
:param wind_barb_length: Length of each wind barb.
:param empty_wind_barb_radius: Radius of circle for 0-metre-per-second wind
barb.
:param fill_empty_wind_barb: Boolean flag. If fill_empty_barb = True,
0-metre-per-second wind barb will be a filled circle. Otherwise, it
will be an empty circle.
:param wind_colour_map: Instance of `matplotlib.pyplot.cm`.
:param colour_minimum_kt: Minimum speed for colour map (kt or nautical miles
per hour).
:param colour_maximum_kt: Maximum speed for colour map (kt or nautical miles
per hour).
"""
error_checking.assert_is_string(storm_id)
error_checking.assert_is_geq(colour_minimum_kt, 0.)
error_checking.assert_is_greater(colour_maximum_kt, colour_minimum_kt)
storm_cell_flags = [this_id == storm_id for this_id in storm_to_winds_table[
tracking_io.STORM_ID_COLUMN].values]
storm_cell_rows = numpy.where(numpy.array(storm_cell_flags))[0]
centroid_latitudes_deg = storm_to_winds_table[
tracking_io.CENTROID_LAT_COLUMN].values[storm_cell_rows]
centroid_longitudes_deg = storm_to_winds_table[
tracking_io.CENTROID_LNG_COLUMN].values[storm_cell_rows]
storm_plotting.plot_storm_track(
basemap_object=basemap_object, axes_object=axes_object,
latitudes_deg=centroid_latitudes_deg,
longitudes_deg=centroid_longitudes_deg, line_colour=storm_colour,
line_width=storm_line_width)
storm_times_unix_sec = storm_to_winds_table[tracking_io.TIME_COLUMN].values[
storm_cell_rows]
first_storm_object_row = storm_cell_rows[numpy.argmin(storm_times_unix_sec)]
last_storm_object_row = storm_cell_rows[numpy.argmax(storm_times_unix_sec)]
first_vertex_dict = polygons.polygon_object_to_vertex_arrays(
storm_to_winds_table[tracking_io.POLYGON_OBJECT_LATLNG_COLUMN].values[
first_storm_object_row])
first_vertex_latitudes_deg = first_vertex_dict[polygons.EXTERIOR_Y_COLUMN]
first_vertex_longitudes_deg = first_vertex_dict[polygons.EXTERIOR_X_COLUMN]
storm_plotting.plot_unfilled_polygon(
basemap_object=basemap_object, axes_object=axes_object,
vertex_latitudes_deg=first_vertex_latitudes_deg,
vertex_longitudes_deg=first_vertex_longitudes_deg,
exterior_colour=storm_colour, exterior_line_width=storm_line_width)
last_vertex_dict = polygons.polygon_object_to_vertex_arrays(
storm_to_winds_table[tracking_io.POLYGON_OBJECT_LATLNG_COLUMN].values[
last_storm_object_row])
last_vertex_latitudes_deg = last_vertex_dict[polygons.EXTERIOR_Y_COLUMN]
last_vertex_longitudes_deg = last_vertex_dict[polygons.EXTERIOR_X_COLUMN]
storm_plotting.plot_unfilled_polygon(
basemap_object=basemap_object, axes_object=axes_object,
vertex_latitudes_deg=last_vertex_latitudes_deg,
vertex_longitudes_deg=last_vertex_longitudes_deg,
exterior_colour=storm_colour, exterior_line_width=storm_line_width)
wind_latitudes_deg = numpy.array([])
wind_longitudes_deg = numpy.array([])
u_winds_m_s01 = numpy.array([])
v_winds_m_s01 = numpy.array([])
for this_row in storm_cell_rows:
wind_latitudes_deg = numpy.concatenate((
wind_latitudes_deg, storm_to_winds_table[
storms_to_winds.WIND_LATITUDES_COLUMN].values[this_row]))
wind_longitudes_deg = numpy.concatenate((
wind_longitudes_deg, storm_to_winds_table[
storms_to_winds.WIND_LONGITUDES_COLUMN].values[this_row]))
u_winds_m_s01 = numpy.concatenate((
u_winds_m_s01, storm_to_winds_table[
storms_to_winds.U_WINDS_COLUMN].values[this_row]))
v_winds_m_s01 = numpy.concatenate((
v_winds_m_s01, storm_to_winds_table[
storms_to_winds.V_WINDS_COLUMN].values[this_row]))
wind_plotting.plot_wind_barbs(
basemap_object=basemap_object, axes_object=axes_object,
latitudes_deg=wind_latitudes_deg, longitudes_deg=wind_longitudes_deg,
u_winds_m_s01=u_winds_m_s01, v_winds_m_s01=v_winds_m_s01,
barb_length=wind_barb_length, empty_barb_radius=empty_wind_barb_radius,
fill_empty_barb=fill_empty_wind_barb, colour_map=wind_colour_map,
colour_minimum_kt=colour_minimum_kt,
colour_maximum_kt=colour_maximum_kt)
def plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01,
v_wind_matrix_m_s01,
model_name,
axes_object,
basemap_object,
grid_id=None,
first_row_in_full_grid=0,
first_column_in_full_grid=0,
plot_every_k_rows=1,
plot_every_k_columns=1,
barb_length=wind_plotting.DEFAULT_BARB_LENGTH,
empty_barb_radius=wind_plotting.DEFAULT_EMPTY_BARB_RADIUS,
fill_empty_barb=wind_plotting.FILL_EMPTY_BARB_DEFAULT,
colour_map=wind_plotting.DEFAULT_COLOUR_MAP,
colour_minimum_kt=wind_plotting.DEFAULT_COLOUR_MINIMUM_KT,
colour_maximum_kt=wind_plotting.DEFAULT_COLOUR_MAXIMUM_KT):
"""Plots wind barbs over subgrid.
:param u_wind_matrix_m_s01: M-by-N numpy array of zonal wind speeds (metres
per second).
:param v_wind_matrix_m_s01: M-by-N numpy array of meridional wind speeds
(metres per second).
:param model_name: See doc for `plot_subgrid`.
:param axes_object: Same.
:param basemap_object: Same.
:param grid_id: Same.
:param first_row_in_full_grid: Row 0 in the subgrid (i.e., row 0 in
`u_wind_matrix_m_s01` and `v_wind_matrix_m_s01` is row
`first_row_in_full_grid` in the full grid).
:param first_column_in_full_grid: Column 0 in the subgrid (i.e., column 0 in
`u_wind_matrix_m_s01` and `v_wind_matrix_m_s01` is column
`first_column_in_full_grid` in the full grid).
:param plot_every_k_rows: Wind barbs will be plotted for every [k]th row in
the subgrid, where k = `plot_every_k_rows`. For example, if
`plot_every_k_rows = 2`, wind barbs will be plotted for rows 0, 2, 4,
etc.
:param plot_every_k_columns: Same as above, but for columns.
:param barb_length: See doc for `wind_plotting.plot_wind_barbs`.
:param empty_barb_radius: Same.
:param fill_empty_barb: Same.
:param colour_map: Same.
:param colour_minimum_kt: Same.
:param colour_maximum_kt: Same.
"""
error_checking.assert_is_real_numpy_array(u_wind_matrix_m_s01)
error_checking.assert_is_numpy_array(u_wind_matrix_m_s01, num_dimensions=2)
error_checking.assert_is_real_numpy_array(v_wind_matrix_m_s01)
these_expected_dim = numpy.array(u_wind_matrix_m_s01.shape, dtype=int)
error_checking.assert_is_numpy_array(v_wind_matrix_m_s01,
exact_dimensions=these_expected_dim)
error_checking.assert_is_integer(plot_every_k_rows)
error_checking.assert_is_geq(plot_every_k_rows, 1)
error_checking.assert_is_integer(plot_every_k_columns)
error_checking.assert_is_geq(plot_every_k_columns, 1)
num_rows_in_subgrid = u_wind_matrix_m_s01.shape[0]
num_columns_in_subgrid = u_wind_matrix_m_s01.shape[1]
coordinate_dict = _get_grid_point_coords(
model_name=model_name,
grid_id=grid_id,
first_row_in_full_grid=first_row_in_full_grid,
last_row_in_full_grid=first_row_in_full_grid + num_rows_in_subgrid - 1,
first_column_in_full_grid=first_column_in_full_grid,
last_column_in_full_grid=first_column_in_full_grid +
num_columns_in_subgrid - 1,
basemap_object=basemap_object)
grid_point_lat_matrix_deg = coordinate_dict[LATITUDE_MATRIX_KEY]
grid_point_lng_matrix_deg = coordinate_dict[LONGITUDE_MATRIX_KEY]
u_wind_matrix_m_s01 = u_wind_matrix_m_s01[::plot_every_k_rows, ::
plot_every_k_columns]
v_wind_matrix_m_s01 = v_wind_matrix_m_s01[::plot_every_k_rows, ::
plot_every_k_columns]
grid_point_lat_matrix_deg = grid_point_lat_matrix_deg[::
plot_every_k_rows, ::
plot_every_k_columns]
grid_point_lng_matrix_deg = grid_point_lng_matrix_deg[::
plot_every_k_rows, ::
plot_every_k_columns]
num_wind_barbs = u_wind_matrix_m_s01.size
u_winds_m_s01 = numpy.reshape(u_wind_matrix_m_s01, num_wind_barbs)
v_winds_m_s01 = numpy.reshape(v_wind_matrix_m_s01, num_wind_barbs)
latitudes_deg = numpy.reshape(grid_point_lat_matrix_deg, num_wind_barbs)
longitudes_deg = numpy.reshape(grid_point_lng_matrix_deg, num_wind_barbs)
nan_flags = numpy.logical_or(numpy.isnan(u_winds_m_s01),
numpy.isnan(v_winds_m_s01))
real_indices = numpy.where(numpy.invert(nan_flags))[0]
wind_plotting.plot_wind_barbs(basemap_object=basemap_object,
axes_object=axes_object,
latitudes_deg=latitudes_deg[real_indices],
longitudes_deg=longitudes_deg[real_indices],
u_winds_m_s01=u_winds_m_s01[real_indices],
v_winds_m_s01=v_winds_m_s01[real_indices],
barb_length=barb_length,
empty_barb_radius=empty_barb_radius,
fill_empty_barb=fill_empty_barb,
colour_map=colour_map,
colour_minimum_kt=colour_minimum_kt,
colour_maximum_kt=colour_maximum_kt)
def plot_wind_linkages(
storm_to_winds_table, basemap_object, axes_object,
storm_colour=storm_plotting.DEFAULT_TRACK_COLOUR,
storm_line_width=storm_plotting.DEFAULT_TRACK_WIDTH,
wind_barb_length=wind_plotting.DEFAULT_BARB_LENGTH,
empty_wind_barb_radius=wind_plotting.DEFAULT_EMPTY_BARB_RADIUS,
fill_empty_wind_barb=wind_plotting.FILL_EMPTY_BARB_DEFAULT,
wind_colour_map=wind_plotting.DEFAULT_COLOUR_MAP,
colour_minimum_kt=wind_plotting.DEFAULT_COLOUR_MINIMUM_KT,
colour_maximum_kt=wind_plotting.DEFAULT_COLOUR_MAXIMUM_KT):
"""Plots wind linkages.
:param storm_to_winds_table: pandas DataFrame with columns listed in
`link_events_to_storms.write_storm_to_winds_table`. This method will
plot linkages for all storm cells in the table.
:param basemap_object: Instance of `mpl_toolkits.basemap.Basemap`.
:param axes_object: Instance of `matplotlib.axes._subplots.AxesSubplot`.
:param storm_colour: Colour for storm tracks and outlines (in any format
accepted by `matplotlib.colors`).
:param storm_line_width: Line width for storm tracks and outlines.
:param wind_barb_length: See doc for `wind_plotting.plot_wind_barbs`.
:param empty_wind_barb_radius: Same.
:param fill_empty_wind_barb: Same.
:param wind_colour_map: Same.
:param colour_minimum_kt: Same.
:param colour_maximum_kt: Same.
"""
storm_object_ids = numpy.array(
storm_to_winds_table[tracking_utils.STORM_ID_COLUMN].values)
storm_cell_ids, object_to_cell_indices = numpy.unique(
storm_object_ids, return_inverse=True)
num_storm_cells = len(storm_cell_ids)
for i in range(num_storm_cells):
these_object_indices = numpy.where(object_to_cell_indices == i)[0]
storm_plotting.plot_storm_track(
basemap_object=basemap_object, axes_object=axes_object,
centroid_latitudes_deg=storm_to_winds_table[
tracking_utils.CENTROID_LAT_COLUMN
].values[these_object_indices],
centroid_longitudes_deg=storm_to_winds_table[
tracking_utils.CENTROID_LNG_COLUMN
].values[these_object_indices],
line_colour=storm_colour, line_width=storm_line_width)
these_wind_latitudes_deg = numpy.concatenate(tuple(
[storm_to_winds_table[
linkage.EVENT_LATITUDES_COLUMN
].values[j] for j in these_object_indices]))
these_wind_longitudes_deg = numpy.concatenate(tuple(
[storm_to_winds_table[
linkage.EVENT_LONGITUDES_COLUMN
].values[j] for j in these_object_indices]))
these_u_winds_m_s01 = numpy.concatenate(tuple(
[storm_to_winds_table[
linkage.U_WINDS_COLUMN
].values[j] for j in these_object_indices]))
these_v_winds_m_s01 = numpy.concatenate(tuple(
[storm_to_winds_table[
linkage.V_WINDS_COLUMN
].values[j] for j in these_object_indices]))
wind_plotting.plot_wind_barbs(
basemap_object=basemap_object, axes_object=axes_object,
latitudes_deg=these_wind_latitudes_deg,
longitudes_deg=these_wind_longitudes_deg,
u_winds_m_s01=these_u_winds_m_s01,
v_winds_m_s01=these_v_winds_m_s01, barb_length=wind_barb_length,
empty_barb_radius=empty_wind_barb_radius,
fill_empty_barb=fill_empty_wind_barb, colour_map=wind_colour_map,
colour_minimum_kt=colour_minimum_kt,
colour_maximum_kt=colour_maximum_kt)