def _plot_storm_outlines_one_time(storm_object_table,
valid_time_unix_sec,
axes_object,
basemap_object,
storm_colour,
storm_opacity,
include_secondary_ids,
output_dir_name,
radar_matrix=None,
radar_field_name=None,
radar_latitudes_deg=None,
radar_longitudes_deg=None):
"""Plots storm outlines (and may underlay radar data) at one time step.
M = number of rows in radar grid
N = number of columns in radar grid
K = number of storm objects
:param storm_object_table: See doc for `storm_plotting.plot_storm_outlines`.
:param valid_time_unix_sec: Will plot storm outlines only at this time.
Will plot tracks up to and including this time.
:param axes_object: Same.
:param basemap_object: Same.
:param storm_colour: Same.
:param storm_opacity: Same.
:param include_secondary_ids: Same.
:param output_dir_name: See documentation at top of file.
:param radar_matrix: M-by-N numpy array of radar values. If
`radar_matrix is None`, radar data will simply not be plotted.
:param radar_field_name: [used only if `radar_matrix is not None`]
See documentation at top of file.
:param radar_latitudes_deg: [used only if `radar_matrix is not None`]
length-M numpy array of grid-point latitudes (deg N).
:param radar_longitudes_deg: [used only if `radar_matrix is not None`]
length-N numpy array of grid-point longitudes (deg E).
"""
min_plot_latitude_deg = basemap_object.llcrnrlat
max_plot_latitude_deg = basemap_object.urcrnrlat
min_plot_longitude_deg = basemap_object.llcrnrlon
max_plot_longitude_deg = basemap_object.urcrnrlon
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)
if radar_matrix is not None:
good_indices = numpy.where(
numpy.logical_and(radar_latitudes_deg >= min_plot_latitude_deg,
radar_latitudes_deg <= max_plot_latitude_deg))[0]
radar_latitudes_deg = radar_latitudes_deg[good_indices]
radar_matrix = radar_matrix[good_indices, :]
good_indices = numpy.where(
numpy.logical_and(
radar_longitudes_deg >= min_plot_longitude_deg,
radar_longitudes_deg <= max_plot_longitude_deg))[0]
radar_longitudes_deg = radar_longitudes_deg[good_indices]
radar_matrix = radar_matrix[:, good_indices]
latitude_spacing_deg = radar_latitudes_deg[1] - radar_latitudes_deg[0]
longitude_spacing_deg = (radar_longitudes_deg[1] -
radar_longitudes_deg[0])
radar_plotting.plot_latlng_grid(
field_matrix=radar_matrix,
field_name=radar_field_name,
axes_object=axes_object,
min_grid_point_latitude_deg=numpy.min(radar_latitudes_deg),
min_grid_point_longitude_deg=numpy.min(radar_longitudes_deg),
latitude_spacing_deg=latitude_spacing_deg,
longitude_spacing_deg=longitude_spacing_deg)
colour_map_object, colour_norm_object = (
radar_plotting.get_default_colour_scheme(radar_field_name))
latitude_range_deg = max_plot_latitude_deg - min_plot_latitude_deg
longitude_range_deg = max_plot_longitude_deg - min_plot_longitude_deg
if latitude_range_deg > longitude_range_deg:
orientation_string = 'vertical'
else:
orientation_string = 'horizontal'
colour_bar_object = plotting_utils.plot_colour_bar(
axes_object_or_matrix=axes_object,
data_matrix=radar_matrix,
colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object,
orientation_string=orientation_string,
extend_min=radar_field_name in radar_plotting.SHEAR_VORT_DIV_NAMES,
extend_max=True,
fraction_of_axis_length=0.9)
colour_bar_object.set_label(
radar_plotting.FIELD_NAME_TO_VERBOSE_DICT[radar_field_name])
valid_time_rows = numpy.where(storm_object_table[
tracking_utils.VALID_TIME_COLUMN].values == valid_time_unix_sec)[0]
line_colour = matplotlib.colors.to_rgba(storm_colour, storm_opacity)
storm_plotting.plot_storm_outlines(
storm_object_table=storm_object_table.iloc[valid_time_rows],
axes_object=axes_object,
basemap_object=basemap_object,
line_colour=line_colour)
storm_plotting.plot_storm_ids(
storm_object_table=storm_object_table.iloc[valid_time_rows],
axes_object=axes_object,
basemap_object=basemap_object,
plot_near_centroids=False,
include_secondary_ids=include_secondary_ids,
font_colour=storm_plotting.DEFAULT_FONT_COLOUR)
storm_plotting.plot_storm_tracks(storm_object_table=storm_object_table,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map_object=None,
line_colour=TRACK_COLOUR)
nice_time_string = time_conversion.unix_sec_to_string(
valid_time_unix_sec, NICE_TIME_FORMAT)
abbrev_time_string = time_conversion.unix_sec_to_string(
valid_time_unix_sec, FILE_NAME_TIME_FORMAT)
pyplot.title('Storm objects at {0:s}'.format(nice_time_string))
output_file_name = '{0:s}/storm_outlines_{1:s}.jpg'.format(
output_dir_name, abbrev_time_string)
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)
def _plot_storm_outlines_one_time(storm_object_table,
valid_time_unix_sec,
warning_table,
axes_object,
basemap_object,
storm_outline_colour,
storm_outline_opacity,
include_secondary_ids,
output_dir_name,
primary_id_to_track_colour=None,
radar_matrix=None,
radar_field_name=None,
radar_latitudes_deg=None,
radar_longitudes_deg=None,
radar_colour_map_object=None):
"""Plots storm outlines (and may underlay radar data) at one time step.
M = number of rows in radar grid
N = number of columns in radar grid
K = number of storm objects
If `primary_id_to_track_colour is None`, all storm tracks will be the same
colour.
:param storm_object_table: See doc for `storm_plotting.plot_storm_outlines`.
:param valid_time_unix_sec: Will plot storm outlines only at this time.
Will plot tracks up to and including this time.
:param warning_table: None or a pandas table with the following columns.
warning_table.start_time_unix_sec: Start time.
warning_table.end_time_unix_sec: End time.
warning_table.polygon_object_latlng: Polygon (instance of
`shapely.geometry.Polygon`) with lat-long coordinates of warning
boundary.
:param axes_object: See doc for `storm_plotting.plot_storm_outlines`.
:param basemap_object: Same.
:param storm_outline_colour: Same.
:param storm_outline_opacity: Same.
:param include_secondary_ids: Same.
:param output_dir_name: See documentation at top of file.
:param primary_id_to_track_colour: Dictionary created by
`_assign_colours_to_storms`. If this is None, all storm tracks will be
the same colour.
:param radar_matrix: M-by-N numpy array of radar values. If
`radar_matrix is None`, radar data will simply not be plotted.
:param radar_field_name: [used only if `radar_matrix is not None`]
See documentation at top of file.
:param radar_latitudes_deg: [used only if `radar_matrix is not None`]
length-M numpy array of grid-point latitudes (deg N).
:param radar_longitudes_deg: [used only if `radar_matrix is not None`]
length-N numpy array of grid-point longitudes (deg E).
:param radar_colour_map_object: [used only if `radar_matrix is not None`]
Colour map (instance of `matplotlib.pyplot.cm`). If None, will use
default for the given field.
"""
# plot_storm_ids = radar_matrix is None or radar_colour_map_object is None
plot_storm_ids = False
min_plot_latitude_deg = basemap_object.llcrnrlat
max_plot_latitude_deg = basemap_object.urcrnrlat
min_plot_longitude_deg = basemap_object.llcrnrlon
max_plot_longitude_deg = basemap_object.urcrnrlon
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)
if radar_matrix is not None:
custom_colour_map = radar_colour_map_object is not None
good_indices = numpy.where(
numpy.logical_and(radar_latitudes_deg >= min_plot_latitude_deg,
radar_latitudes_deg <= max_plot_latitude_deg))[0]
radar_latitudes_deg = radar_latitudes_deg[good_indices]
radar_matrix = radar_matrix[good_indices, :]
good_indices = numpy.where(
numpy.logical_and(
radar_longitudes_deg >= min_plot_longitude_deg,
radar_longitudes_deg <= max_plot_longitude_deg))[0]
radar_longitudes_deg = radar_longitudes_deg[good_indices]
radar_matrix = radar_matrix[:, good_indices]
latitude_spacing_deg = radar_latitudes_deg[1] - radar_latitudes_deg[0]
longitude_spacing_deg = (radar_longitudes_deg[1] -
radar_longitudes_deg[0])
if radar_colour_map_object is None:
colour_map_object, colour_norm_object = (
radar_plotting.get_default_colour_scheme(radar_field_name))
else:
colour_map_object = radar_colour_map_object
colour_norm_object = radar_plotting.get_default_colour_scheme(
radar_field_name)[-1]
this_ratio = radar_plotting._field_to_plotting_units(
field_matrix=1., field_name=radar_field_name)
colour_norm_object = pyplot.Normalize(
colour_norm_object.vmin / this_ratio,
colour_norm_object.vmax / this_ratio)
radar_plotting.plot_latlng_grid(
field_matrix=radar_matrix,
field_name=radar_field_name,
axes_object=axes_object,
min_grid_point_latitude_deg=numpy.min(radar_latitudes_deg),
min_grid_point_longitude_deg=numpy.min(radar_longitudes_deg),
latitude_spacing_deg=latitude_spacing_deg,
longitude_spacing_deg=longitude_spacing_deg,
colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object)
latitude_range_deg = max_plot_latitude_deg - min_plot_latitude_deg
longitude_range_deg = max_plot_longitude_deg - min_plot_longitude_deg
if latitude_range_deg > longitude_range_deg:
orientation_string = 'vertical'
else:
orientation_string = 'horizontal'
colour_bar_object = plotting_utils.plot_colour_bar(
axes_object_or_matrix=axes_object,
data_matrix=radar_matrix,
colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object,
orientation_string=orientation_string,
padding=0.05,
extend_min=radar_field_name in radar_plotting.SHEAR_VORT_DIV_NAMES,
extend_max=True,
fraction_of_axis_length=1.)
radar_field_name_verbose = radar_utils.field_name_to_verbose(
field_name=radar_field_name, include_units=True)
radar_field_name_verbose = radar_field_name_verbose.replace(
'm ASL', 'kft ASL')
colour_bar_object.set_label(radar_field_name_verbose)
if custom_colour_map:
tick_values = colour_bar_object.get_ticks()
tick_label_strings = ['{0:.1f}'.format(v) for v in tick_values]
colour_bar_object.set_ticks(tick_values)
colour_bar_object.set_ticklabels(tick_label_strings)
valid_time_rows = numpy.where(storm_object_table[
tracking_utils.VALID_TIME_COLUMN].values == valid_time_unix_sec)[0]
this_colour = matplotlib.colors.to_rgba(storm_outline_colour,
storm_outline_opacity)
storm_plotting.plot_storm_outlines(
storm_object_table=storm_object_table.iloc[valid_time_rows],
axes_object=axes_object,
basemap_object=basemap_object,
line_colour=this_colour)
if plot_storm_ids:
storm_plotting.plot_storm_ids(
storm_object_table=storm_object_table.iloc[valid_time_rows],
axes_object=axes_object,
basemap_object=basemap_object,
plot_near_centroids=False,
include_secondary_ids=include_secondary_ids,
font_colour=storm_plotting.DEFAULT_FONT_COLOUR)
if warning_table is not None:
warning_indices = numpy.where(
numpy.logical_and(
warning_table[WARNING_START_TIME_KEY].values <=
valid_time_unix_sec, warning_table[WARNING_END_TIME_KEY].values
>= valid_time_unix_sec))[0]
for k in warning_indices:
this_vertex_dict = polygons.polygon_object_to_vertex_arrays(
warning_table[WARNING_LATLNG_POLYGON_KEY].values[k])
these_latitudes_deg = this_vertex_dict[polygons.EXTERIOR_Y_COLUMN]
these_longitudes_deg = this_vertex_dict[polygons.EXTERIOR_X_COLUMN]
these_latitude_flags = numpy.logical_and(
these_latitudes_deg >= min_plot_latitude_deg,
these_latitudes_deg <= max_plot_latitude_deg)
these_longitude_flags = numpy.logical_and(
these_longitudes_deg >= min_plot_longitude_deg,
these_longitudes_deg <= max_plot_longitude_deg)
these_coord_flags = numpy.logical_and(these_latitude_flags,
these_longitude_flags)
if not numpy.any(these_coord_flags):
continue
these_x_metres, these_y_metres = basemap_object(
these_longitudes_deg, these_latitudes_deg)
axes_object.plot(these_x_metres,
these_y_metres,
color=this_colour,
linestyle='dashed',
linewidth=storm_plotting.DEFAULT_POLYGON_WIDTH)
axes_object.text(numpy.mean(these_x_metres),
numpy.mean(these_y_metres),
'W{0:d}'.format(k),
fontsize=storm_plotting.DEFAULT_FONT_SIZE,
fontweight='bold',
color=this_colour,
horizontalalignment='center',
verticalalignment='center')
these_sec_id_strings = (
warning_table[LINKED_SECONDARY_IDS_KEY].values[k])
if len(these_sec_id_strings) == 0:
continue
these_object_indices = numpy.array([], dtype=int)
for this_sec_id_string in these_sec_id_strings:
these_subindices = numpy.where(
storm_object_table[tracking_utils.SECONDARY_ID_COLUMN].
values[valid_time_rows] == this_sec_id_string)[0]
these_object_indices = numpy.concatenate(
(these_object_indices, valid_time_rows[these_subindices]))
for i in these_object_indices:
this_vertex_dict = polygons.polygon_object_to_vertex_arrays(
storm_object_table[
tracking_utils.LATLNG_POLYGON_COLUMN].values[i])
these_x_metres, these_y_metres = basemap_object(
this_vertex_dict[polygons.EXTERIOR_X_COLUMN],
this_vertex_dict[polygons.EXTERIOR_Y_COLUMN])
axes_object.text(numpy.mean(these_x_metres),
numpy.mean(these_y_metres),
'W{0:d}'.format(k),
fontsize=storm_plotting.DEFAULT_FONT_SIZE,
fontweight='bold',
color=this_colour,
horizontalalignment='center',
verticalalignment='center')
if primary_id_to_track_colour is None:
storm_plotting.plot_storm_tracks(storm_object_table=storm_object_table,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map_object=None,
constant_colour=DEFAULT_TRACK_COLOUR)
else:
for this_primary_id_string in primary_id_to_track_colour:
this_storm_object_table = storm_object_table.loc[
storm_object_table[tracking_utils.PRIMARY_ID_COLUMN] ==
this_primary_id_string]
if len(this_storm_object_table.index) == 0:
continue
storm_plotting.plot_storm_tracks(
storm_object_table=this_storm_object_table,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map_object=None,
constant_colour=primary_id_to_track_colour[
this_primary_id_string])
nice_time_string = time_conversion.unix_sec_to_string(
valid_time_unix_sec, NICE_TIME_FORMAT)
abbrev_time_string = time_conversion.unix_sec_to_string(
valid_time_unix_sec, FILE_NAME_TIME_FORMAT)
pyplot.title('Storm objects at {0:s}'.format(nice_time_string))
output_file_name = '{0:s}/storm_outlines_{1:s}.jpg'.format(
output_dir_name, abbrev_time_string)
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_example_one_time(
storm_object_table, full_id_string, valid_time_unix_sec,
tornado_table, top_myrorss_dir_name, radar_field_name,
radar_height_m_asl, latitude_limits_deg, longitude_limits_deg):
"""Plots one example with surrounding context at one time.
:param storm_object_table: pandas DataFrame, containing only storm objects
at one time with the relevant primary ID. Columns are documented in
`storm_tracking_io.write_file`.
:param full_id_string: Full ID of storm of interest.
:param valid_time_unix_sec: Valid time.
:param tornado_table: pandas DataFrame created by
`linkage._read_input_tornado_reports`.
:param top_myrorss_dir_name: See documentation at top of file.
:param radar_field_name: Same.
:param radar_height_m_asl: Same.
:param latitude_limits_deg: See doc for `_get_plotting_limits`.
:param longitude_limits_deg: Same.
"""
min_plot_latitude_deg = latitude_limits_deg[0]
max_plot_latitude_deg = latitude_limits_deg[1]
min_plot_longitude_deg = longitude_limits_deg[0]
max_plot_longitude_deg = longitude_limits_deg[1]
radar_file_name = myrorss_and_mrms_io.find_raw_file_inexact_time(
top_directory_name=top_myrorss_dir_name,
desired_time_unix_sec=valid_time_unix_sec,
spc_date_string=time_conversion.time_to_spc_date_string(
valid_time_unix_sec),
data_source=radar_utils.MYRORSS_SOURCE_ID,
field_name=radar_field_name, height_m_asl=radar_height_m_asl,
max_time_offset_sec=
myrorss_and_mrms_io.DEFAULT_MAX_TIME_OFFSET_FOR_NON_SHEAR_SEC,
raise_error_if_missing=True)
print('Reading data from: "{0:s}"...'.format(radar_file_name))
radar_metadata_dict = myrorss_and_mrms_io.read_metadata_from_raw_file(
netcdf_file_name=radar_file_name,
data_source=radar_utils.MYRORSS_SOURCE_ID)
sparse_grid_table = (
myrorss_and_mrms_io.read_data_from_sparse_grid_file(
netcdf_file_name=radar_file_name,
field_name_orig=radar_metadata_dict[
myrorss_and_mrms_io.FIELD_NAME_COLUMN_ORIG],
data_source=radar_utils.MYRORSS_SOURCE_ID,
sentinel_values=radar_metadata_dict[
radar_utils.SENTINEL_VALUE_COLUMN]
)
)
radar_matrix, grid_point_latitudes_deg, grid_point_longitudes_deg = (
radar_s2f.sparse_to_full_grid(
sparse_grid_table=sparse_grid_table,
metadata_dict=radar_metadata_dict)
)
radar_matrix = numpy.flip(radar_matrix, axis=0)
grid_point_latitudes_deg = grid_point_latitudes_deg[::-1]
axes_object, basemap_object = (
plotting_utils.create_equidist_cylindrical_map(
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, resolution_string='h'
)[1:]
)
plotting_utils.plot_coastlines(
basemap_object=basemap_object, axes_object=axes_object,
line_colour=plotting_utils.DEFAULT_COUNTRY_COLOUR)
plotting_utils.plot_countries(
basemap_object=basemap_object, axes_object=axes_object)
plotting_utils.plot_states_and_provinces(
basemap_object=basemap_object, axes_object=axes_object)
plotting_utils.plot_parallels(
basemap_object=basemap_object, axes_object=axes_object,
num_parallels=NUM_PARALLELS, line_width=0)
plotting_utils.plot_meridians(
basemap_object=basemap_object, axes_object=axes_object,
num_meridians=NUM_MERIDIANS, line_width=0)
radar_plotting.plot_latlng_grid(
field_matrix=radar_matrix, field_name=radar_field_name,
axes_object=axes_object,
min_grid_point_latitude_deg=numpy.min(grid_point_latitudes_deg),
min_grid_point_longitude_deg=numpy.min(grid_point_longitudes_deg),
latitude_spacing_deg=numpy.diff(grid_point_latitudes_deg[:2])[0],
longitude_spacing_deg=numpy.diff(grid_point_longitudes_deg[:2])[0]
)
colour_map_object, colour_norm_object = (
radar_plotting.get_default_colour_scheme(radar_field_name)
)
plotting_utils.plot_colour_bar(
axes_object_or_matrix=axes_object, data_matrix=radar_matrix,
colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object, orientation_string='horizontal',
padding=0.05, extend_min=False, extend_max=True,
fraction_of_axis_length=0.8)
first_list, second_list = temporal_tracking.full_to_partial_ids(
[full_id_string]
)
primary_id_string = first_list[0]
secondary_id_string = second_list[0]
# Plot outlines of unrelated storms (with different primary IDs).
this_storm_object_table = storm_object_table.loc[
storm_object_table[tracking_utils.PRIMARY_ID_COLUMN] !=
primary_id_string
]
storm_plotting.plot_storm_outlines(
storm_object_table=this_storm_object_table, axes_object=axes_object,
basemap_object=basemap_object, line_width=AUXILIARY_STORM_WIDTH,
line_colour='k', line_style='dashed')
# Plot outlines of related storms (with the same primary ID).
this_storm_object_table = storm_object_table.loc[
(storm_object_table[tracking_utils.PRIMARY_ID_COLUMN] ==
primary_id_string) &
(storm_object_table[tracking_utils.SECONDARY_ID_COLUMN] !=
secondary_id_string)
]
this_num_storm_objects = len(this_storm_object_table.index)
if this_num_storm_objects > 0:
storm_plotting.plot_storm_outlines(
storm_object_table=this_storm_object_table, axes_object=axes_object,
basemap_object=basemap_object, line_width=AUXILIARY_STORM_WIDTH,
line_colour='k', line_style='solid'
)
for j in range(len(this_storm_object_table)):
axes_object.text(
this_storm_object_table[
tracking_utils.CENTROID_LONGITUDE_COLUMN
].values[j],
this_storm_object_table[
tracking_utils.CENTROID_LATITUDE_COLUMN
].values[j],
'P',
fontsize=MAIN_FONT_SIZE, color=FONT_COLOUR, fontweight='bold',
horizontalalignment='center', verticalalignment='center'
)
# Plot outline of storm of interest (same secondary ID).
this_storm_object_table = storm_object_table.loc[
storm_object_table[tracking_utils.SECONDARY_ID_COLUMN] ==
secondary_id_string
]
storm_plotting.plot_storm_outlines(
storm_object_table=this_storm_object_table, axes_object=axes_object,
basemap_object=basemap_object, line_width=MAIN_STORM_WIDTH,
line_colour='k', line_style='solid')
this_num_storm_objects = len(this_storm_object_table.index)
plot_forecast = (
this_num_storm_objects > 0 and
FORECAST_PROBABILITY_COLUMN in list(this_storm_object_table)
)
if plot_forecast:
label_string = 'Prob = {0:.3f}\nat {1:s}'.format(
this_storm_object_table[FORECAST_PROBABILITY_COLUMN].values[0],
time_conversion.unix_sec_to_string(
valid_time_unix_sec, TORNADO_TIME_FORMAT)
)
axes_object.set_title(
label_string.replace('\n', ' '), fontsize=TITLE_FONT_SIZE
)
tornado_id_strings = tornado_table[tornado_io.TORNADO_ID_COLUMN].values
for this_tornado_id_string in numpy.unique(tornado_id_strings):
these_rows = numpy.where(
tornado_id_strings == this_tornado_id_string
)[0]
this_tornado_table = tornado_table.iloc[these_rows].sort_values(
linkage.EVENT_TIME_COLUMN, axis=0, ascending=True, inplace=False
)
_plot_one_tornado(
tornado_table=this_tornado_table, axes_object=axes_object
)
def _plot_one_example_one_time(storm_object_table, full_id_string,
valid_time_unix_sec, tornado_table,
top_myrorss_dir_name, radar_field_name,
radar_height_m_asl, latitude_limits_deg,
longitude_limits_deg):
"""Plots one example with surrounding context at one time.
:param storm_object_table: pandas DataFrame, containing only storm objects
at one time with the relevant primary ID. Columns are documented in
`storm_tracking_io.write_file`.
:param full_id_string: Full ID of storm of interest.
:param valid_time_unix_sec: Valid time.
:param tornado_table: pandas DataFrame created by
`linkage._read_input_tornado_reports`.
:param top_myrorss_dir_name: See documentation at top of file.
:param radar_field_name: Same.
:param radar_height_m_asl: Same.
:param latitude_limits_deg: See doc for `_get_plotting_limits`.
:param longitude_limits_deg: Same.
"""
min_plot_latitude_deg = latitude_limits_deg[0]
max_plot_latitude_deg = latitude_limits_deg[1]
min_plot_longitude_deg = longitude_limits_deg[0]
max_plot_longitude_deg = longitude_limits_deg[1]
radar_file_name = myrorss_and_mrms_io.find_raw_file(
top_directory_name=top_myrorss_dir_name,
spc_date_string=time_conversion.time_to_spc_date_string(
valid_time_unix_sec),
unix_time_sec=valid_time_unix_sec,
data_source=radar_utils.MYRORSS_SOURCE_ID,
field_name=radar_field_name,
height_m_asl=radar_height_m_asl,
raise_error_if_missing=True)
print('Reading data from: "{0:s}"...'.format(radar_file_name))
radar_metadata_dict = myrorss_and_mrms_io.read_metadata_from_raw_file(
netcdf_file_name=radar_file_name,
data_source=radar_utils.MYRORSS_SOURCE_ID)
sparse_grid_table = (myrorss_and_mrms_io.read_data_from_sparse_grid_file(
netcdf_file_name=radar_file_name,
field_name_orig=radar_metadata_dict[
myrorss_and_mrms_io.FIELD_NAME_COLUMN_ORIG],
data_source=radar_utils.MYRORSS_SOURCE_ID,
sentinel_values=radar_metadata_dict[radar_utils.SENTINEL_VALUE_COLUMN])
)
radar_matrix, grid_point_latitudes_deg, grid_point_longitudes_deg = (
radar_s2f.sparse_to_full_grid(sparse_grid_table=sparse_grid_table,
metadata_dict=radar_metadata_dict))
radar_matrix = numpy.flip(radar_matrix, axis=0)
grid_point_latitudes_deg = grid_point_latitudes_deg[::-1]
axes_object, basemap_object = (
plotting_utils.create_equidist_cylindrical_map(
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,
resolution_string='i')[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)
radar_plotting.plot_latlng_grid(
field_matrix=radar_matrix,
field_name=radar_field_name,
axes_object=axes_object,
min_grid_point_latitude_deg=numpy.min(grid_point_latitudes_deg),
min_grid_point_longitude_deg=numpy.min(grid_point_longitudes_deg),
latitude_spacing_deg=numpy.diff(grid_point_latitudes_deg[:2])[0],
longitude_spacing_deg=numpy.diff(grid_point_longitudes_deg[:2])[0])
colour_map_object, colour_norm_object = (
radar_plotting.get_default_colour_scheme(radar_field_name))
plotting_utils.plot_colour_bar(axes_object_or_matrix=axes_object,
data_matrix=radar_matrix,
colour_map_object=colour_map_object,
colour_norm_object=colour_norm_object,
orientation_string='horizontal',
extend_min=False,
extend_max=True,
fraction_of_axis_length=0.8)
first_list, second_list = temporal_tracking.full_to_partial_ids(
[full_id_string])
primary_id_string = first_list[0]
secondary_id_string = second_list[0]
# Plot outlines of unrelated storms (with different primary IDs).
this_storm_object_table = storm_object_table.loc[storm_object_table[
tracking_utils.PRIMARY_ID_COLUMN] != primary_id_string]
storm_plotting.plot_storm_outlines(
storm_object_table=this_storm_object_table,
axes_object=axes_object,
basemap_object=basemap_object,
line_width=2,
line_colour='k',
line_style='dashed')
# Plot outlines of related storms (with the same primary ID).
this_storm_object_table = storm_object_table.loc[
(storm_object_table[tracking_utils.PRIMARY_ID_COLUMN] ==
primary_id_string) & (storm_object_table[
tracking_utils.SECONDARY_ID_COLUMN] != secondary_id_string)]
this_num_storm_objects = len(this_storm_object_table.index)
if this_num_storm_objects > 0:
storm_plotting.plot_storm_outlines(
storm_object_table=this_storm_object_table,
axes_object=axes_object,
basemap_object=basemap_object,
line_width=2,
line_colour='k',
line_style='solid')
for j in range(len(this_storm_object_table)):
axes_object.text(
this_storm_object_table[
tracking_utils.CENTROID_LONGITUDE_COLUMN].values[j],
this_storm_object_table[
tracking_utils.CENTROID_LATITUDE_COLUMN].values[j],
'P',
fontsize=FONT_SIZE,
color=FONT_COLOUR,
fontweight='bold',
horizontalalignment='center',
verticalalignment='center')
# Plot outline of storm of interest (same secondary ID).
this_storm_object_table = storm_object_table.loc[storm_object_table[
tracking_utils.SECONDARY_ID_COLUMN] == secondary_id_string]
storm_plotting.plot_storm_outlines(
storm_object_table=this_storm_object_table,
axes_object=axes_object,
basemap_object=basemap_object,
line_width=4,
line_colour='k',
line_style='solid')
this_num_storm_objects = len(this_storm_object_table.index)
plot_forecast = (this_num_storm_objects > 0 and FORECAST_PROBABILITY_COLUMN
in list(this_storm_object_table))
if plot_forecast:
this_polygon_object_latlng = this_storm_object_table[
tracking_utils.LATLNG_POLYGON_COLUMN].values[0]
this_latitude_deg = numpy.min(
numpy.array(this_polygon_object_latlng.exterior.xy[1]))
this_longitude_deg = this_storm_object_table[
tracking_utils.CENTROID_LONGITUDE_COLUMN].values[0]
label_string = 'Prob = {0:.3f}\nat {1:s}'.format(
this_storm_object_table[FORECAST_PROBABILITY_COLUMN].values[0],
time_conversion.unix_sec_to_string(valid_time_unix_sec,
TORNADO_TIME_FORMAT))
bounding_box_dict = {
'facecolor':
plotting_utils.colour_from_numpy_to_tuple(
PROBABILITY_BACKGROUND_COLOUR),
'alpha':
PROBABILITY_BACKGROUND_OPACITY,
'edgecolor':
'k',
'linewidth':
1
}
axes_object.text(this_longitude_deg,
this_latitude_deg,
label_string,
fontsize=FONT_SIZE,
color=plotting_utils.colour_from_numpy_to_tuple(
PROBABILITY_FONT_COLOUR),
fontweight='bold',
bbox=bounding_box_dict,
horizontalalignment='center',
verticalalignment='top',
zorder=1e10)
tornado_latitudes_deg = tornado_table[linkage.EVENT_LATITUDE_COLUMN].values
tornado_longitudes_deg = tornado_table[
linkage.EVENT_LONGITUDE_COLUMN].values
tornado_times_unix_sec = tornado_table[linkage.EVENT_TIME_COLUMN].values
tornado_time_strings = [
time_conversion.unix_sec_to_string(t, TORNADO_TIME_FORMAT)
for t in tornado_times_unix_sec
]
axes_object.plot(tornado_longitudes_deg,
tornado_latitudes_deg,
linestyle='None',
marker=TORNADO_MARKER_TYPE,
markersize=TORNADO_MARKER_SIZE,
markeredgewidth=TORNADO_MARKER_EDGE_WIDTH,
markerfacecolor=plotting_utils.colour_from_numpy_to_tuple(
TORNADO_MARKER_COLOUR),
markeredgecolor=plotting_utils.colour_from_numpy_to_tuple(
TORNADO_MARKER_COLOUR))
num_tornadoes = len(tornado_latitudes_deg)
for j in range(num_tornadoes):
axes_object.text(tornado_longitudes_deg[j] + 0.02,
tornado_latitudes_deg[j] - 0.02,
tornado_time_strings[j],
fontsize=FONT_SIZE,
color=FONT_COLOUR,
fontweight='bold',
horizontalalignment='left',
verticalalignment='top')
def _run(top_radar_dir_name, top_echo_classifn_dir_name, valid_time_string,
min_latitude_deg, max_latitude_deg, min_longitude_deg,
max_longitude_deg, output_dir_name):
"""Makes figure to explain storm detection.
This is effectively the main method.
:param top_radar_dir_name: See documentation at top of file.
:param top_echo_classifn_dir_name: Same.
:param valid_time_string: Same.
:param min_latitude_deg: Same.
:param max_latitude_deg: Same.
:param min_longitude_deg: Same.
:param max_longitude_deg: Same.
:param output_dir_name: Same.
"""
file_system_utils.mkdir_recursive_if_necessary(
directory_name=output_dir_name
)
valid_time_unix_sec = time_conversion.string_to_unix_sec(
valid_time_string, TIME_FORMAT
)
spc_date_string = time_conversion.time_to_spc_date_string(
valid_time_unix_sec
)
num_trials = len(MIN_POLYGON_SIZES_PX)
tracking_dir_names = [None] * num_trials
for k in range(num_trials):
tracking_dir_names[k] = (
'{0:s}/tracking/min-polygon-size-px={1:d}_recompute-centroids={2:d}'
).format(
output_dir_name, MIN_POLYGON_SIZES_PX[k],
int(RECOMPUTE_CENTROID_FLAGS[k])
)
echo_top_tracking.run_tracking(
top_radar_dir_name=top_radar_dir_name,
top_output_dir_name=tracking_dir_names[k],
first_spc_date_string=spc_date_string,
last_spc_date_string=spc_date_string,
first_time_unix_sec=valid_time_unix_sec,
last_time_unix_sec=valid_time_unix_sec + 1,
top_echo_classifn_dir_name=top_echo_classifn_dir_name,
min_polygon_size_pixels=MIN_POLYGON_SIZES_PX[k],
recompute_centroids=RECOMPUTE_CENTROID_FLAGS[k]
)
print(SEPARATOR_STRING)
echo_top_file_name = myrorss_and_mrms_io.find_raw_file(
top_directory_name=top_radar_dir_name,
unix_time_sec=valid_time_unix_sec, spc_date_string=spc_date_string,
field_name=radar_utils.ECHO_TOP_40DBZ_NAME,
data_source=radar_utils.MYRORSS_SOURCE_ID, raise_error_if_missing=True
)
print('Reading data from: "{0:s}"...'.format(echo_top_file_name))
metadata_dict = myrorss_and_mrms_io.read_metadata_from_raw_file(
netcdf_file_name=echo_top_file_name,
data_source=radar_utils.MYRORSS_SOURCE_ID
)
sparse_grid_table = myrorss_and_mrms_io.read_data_from_sparse_grid_file(
netcdf_file_name=echo_top_file_name,
field_name_orig=metadata_dict[
myrorss_and_mrms_io.FIELD_NAME_COLUMN_ORIG],
data_source=radar_utils.MYRORSS_SOURCE_ID,
sentinel_values=metadata_dict[radar_utils.SENTINEL_VALUE_COLUMN]
)
echo_top_matrix_km_asl, radar_latitudes_deg, radar_longitudes_deg = (
radar_s2f.sparse_to_full_grid(
sparse_grid_table=sparse_grid_table, metadata_dict=metadata_dict)
)
echo_top_matrix_km_asl = numpy.flip(echo_top_matrix_km_asl, axis=0)
radar_latitudes_deg = radar_latitudes_deg[::-1]
echo_classifn_file_name = echo_classifn.find_classification_file(
top_directory_name=top_echo_classifn_dir_name,
valid_time_unix_sec=valid_time_unix_sec,
desire_zipped=True, allow_zipped_or_unzipped=True,
raise_error_if_missing=True
)
print('Reading data from: "{0:s}"...'.format(echo_classifn_file_name))
convective_flag_matrix = echo_classifn.read_classifications(
echo_classifn_file_name
)[0]
good_indices = numpy.where(numpy.logical_and(
radar_latitudes_deg >= min_latitude_deg,
radar_latitudes_deg <= max_latitude_deg
))[0]
echo_top_matrix_km_asl = echo_top_matrix_km_asl[good_indices, ...]
convective_flag_matrix = convective_flag_matrix[good_indices, ...]
radar_latitudes_deg = radar_latitudes_deg[good_indices]
good_indices = numpy.where(numpy.logical_and(
radar_longitudes_deg >= min_longitude_deg,
radar_longitudes_deg <= max_longitude_deg
))[0]
echo_top_matrix_km_asl = echo_top_matrix_km_asl[..., good_indices]
convective_flag_matrix = convective_flag_matrix[..., good_indices]
radar_longitudes_deg = radar_longitudes_deg[good_indices]
this_figure_object, this_axes_object = _plot_echo_tops(
echo_top_matrix_km_asl=echo_top_matrix_km_asl,
latitudes_deg=radar_latitudes_deg, longitudes_deg=radar_longitudes_deg,
plot_colour_bar=False, convective_flag_matrix=None
)[:2]
this_axes_object.set_title('All echoes')
plotting_utils.label_axes(axes_object=this_axes_object, label_string='(a)')
panel_file_names = [
'{0:s}/before_echo_classification.jpg'.format(output_dir_name)
]
print('Saving figure to: "{0:s}"...'.format(panel_file_names[-1]))
this_figure_object.savefig(
panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0, bbox_inches='tight'
)
pyplot.close(this_figure_object)
this_figure_object, this_axes_object = _plot_echo_tops(
echo_top_matrix_km_asl=echo_top_matrix_km_asl,
latitudes_deg=radar_latitudes_deg, longitudes_deg=radar_longitudes_deg,
plot_colour_bar=False, convective_flag_matrix=convective_flag_matrix
)[:2]
this_axes_object.set_title('Convective echoes only')
plotting_utils.label_axes(axes_object=this_axes_object, label_string='(b)')
panel_file_names.append(
'{0:s}/after_echo_classification.jpg'.format(output_dir_name)
)
print('Saving figure to: "{0:s}"...'.format(panel_file_names[-1]))
this_figure_object.savefig(
panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0, bbox_inches='tight'
)
pyplot.close(this_figure_object)
letter_label = 'b'
for k in range(num_trials):
this_tracking_file_name = tracking_io.find_file(
top_tracking_dir_name=tracking_dir_names[k],
tracking_scale_metres2=
echo_top_tracking.DUMMY_TRACKING_SCALE_METRES2,
source_name=tracking_utils.SEGMOTION_NAME,
valid_time_unix_sec=valid_time_unix_sec,
spc_date_string=spc_date_string,
raise_error_if_missing=True
)
print('Reading data from: "{0:s}"...'.format(this_tracking_file_name))
this_storm_object_table = tracking_io.read_file(this_tracking_file_name)
this_figure_object, this_axes_object, this_basemap_object = (
_plot_echo_tops(
echo_top_matrix_km_asl=echo_top_matrix_km_asl,
latitudes_deg=radar_latitudes_deg,
longitudes_deg=radar_longitudes_deg, plot_colour_bar=k > 0,
convective_flag_matrix=convective_flag_matrix)
)
storm_plotting.plot_storm_outlines(
storm_object_table=this_storm_object_table,
axes_object=this_axes_object, basemap_object=this_basemap_object,
line_width=POLYGON_WIDTH, line_colour=POLYGON_COLOUR
)
these_x_metres, these_y_metres = this_basemap_object(
this_storm_object_table[
tracking_utils.CENTROID_LONGITUDE_COLUMN].values,
this_storm_object_table[
tracking_utils.CENTROID_LATITUDE_COLUMN].values
)
this_axes_object.plot(
these_x_metres, these_y_metres, linestyle='None',
marker=MARKER_TYPE, markersize=MARKER_SIZE,
markerfacecolor=MARKER_COLOUR, markeredgecolor=MARKER_COLOUR,
markeredgewidth=MARKER_EDGE_WIDTH
)
this_title_string = (
'Minimum size = {0:d} GP, {1:s} storm centers'
).format(
MIN_POLYGON_SIZES_PX[k],
'recomputed' if RECOMPUTE_CENTROID_FLAGS[k] else 'original'
)
this_axes_object.set_title(this_title_string)
letter_label = chr(ord(letter_label) + 1)
plotting_utils.label_axes(
axes_object=this_axes_object,
label_string='({0:s})'.format(letter_label)
)
panel_file_names.append(
'{0:s}/detection{1:d}.jpg'.format(output_dir_name, k)
)
print('Saving figure to: "{0:s}"...'.format(panel_file_names[-1]))
this_figure_object.savefig(
panel_file_names[-1], dpi=FIGURE_RESOLUTION_DPI,
pad_inches=0, bbox_inches='tight'
)
pyplot.close(this_figure_object)
concat_file_name = '{0:s}/storm_detection.jpg'.format(output_dir_name)
print('Concatenating panels to: "{0:s}"...'.format(concat_file_name))
imagemagick_utils.concatenate_images(
input_file_names=panel_file_names, output_file_name=concat_file_name,
num_panel_rows=3, num_panel_columns=2
)