def _read_actual_polylines(top_input_dir_name, unix_times_sec):
"""Reads actual fronts (polylines) for each time step.
:param top_input_dir_name: See documentation at top of file.
:param unix_times_sec: 1-D numpy array of valid times.
:return: polyline_table: See doc for `fronts_io.write_polylines_to_file`.
"""
list_of_polyline_tables = []
for this_time_unix_sec in unix_times_sec:
this_file_name = fronts_io.find_file_for_one_time(
top_directory_name=top_input_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)
list_of_polyline_tables.append(
fronts_io.read_polylines_from_file(this_file_name)
)
if len(list_of_polyline_tables) == 1:
continue
list_of_polyline_tables[-1] = list_of_polyline_tables[-1].align(
list_of_polyline_tables[0], axis=1)[0]
# print 'Removing fronts in masked area...'
# return front_utils.remove_polylines_in_masked_area(
# polyline_table=polyline_table, narr_mask_matrix=narr_mask_matrix)
return pandas.concat(
list_of_polyline_tables, axis=0, ignore_index=True)
def _run():
"""Plots dilation of WPC fronts.
This is effectively the main method.
"""
print 'Reading data from: "{0:s}"...'.format(FRONT_LINE_FILE_NAME)
front_line_table = fronts_io.read_polylines_from_file(FRONT_LINE_FILE_NAME)
print 'Reading data from: "{0:s}"...'.format(FRONTAL_GRID_FILE_NAME)
frontal_grid_table = fronts_io.read_narr_grids_from_file(
FRONTAL_GRID_FILE_NAME)
num_grid_rows, num_grid_columns = nwp_model_utils.get_grid_dimensions(
model_name=nwp_model_utils.NARR_MODEL_NAME)
ternary_front_matrix = ml_utils.front_table_to_images(
frontal_grid_table=frontal_grid_table,
num_rows_per_image=num_grid_rows,
num_columns_per_image=num_grid_columns)
_plot_fronts(front_line_table=front_line_table,
ternary_front_matrix=ternary_front_matrix,
title_string='Observed fronts before dilation',
annotation_string='(a)',
output_file_name=BEFORE_FILE_NAME)
ternary_front_matrix = ml_utils.dilate_ternary_target_images(
target_matrix=ternary_front_matrix,
dilation_distance_metres=DILATION_DISTANCE_METRES,
verbose=False)
_plot_fronts(front_line_table=front_line_table,
ternary_front_matrix=ternary_front_matrix,
title_string='Observed fronts after dilation',
annotation_string='(b)',
output_file_name=AFTER_FILE_NAME)
print 'Concatenating figures to: "{0:s}"...'.format(CONCAT_FILE_NAME)
imagemagick_utils.concatenate_images(
input_file_names=[BEFORE_FILE_NAME, AFTER_FILE_NAME],
output_file_name=CONCAT_FILE_NAME,
num_panel_rows=1,
num_panel_columns=2)
imagemagick_utils.resize_image(input_file_name=CONCAT_FILE_NAME,
output_file_name=CONCAT_FILE_NAME,
output_size_pixels=CONCAT_SIZE_PIXELS)
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():
"""Plots input example.
This is effectively the main method.
:return: figure_file_name: Path to output file (where the figure was saved).
"""
valid_time_unix_sec = time_conversion.string_to_unix_sec(
VALID_TIME_STRING, TIME_FORMAT)
front_file_name = fronts_io.find_file_for_one_time(
top_directory_name=TOP_FRONT_DIR_NAME,
file_type=fronts_io.POLYLINE_FILE_TYPE,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(front_file_name)
front_line_table = fronts_io.read_polylines_from_file(front_file_name)
num_narr_fields = len(NARR_FIELD_NAMES)
narr_matrix_by_field = [numpy.array([])] * num_narr_fields
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
this_directory_name = '{0:s}/earth_relative_wind'.format(
TOP_NARR_DIRECTORY_NAME)
else:
this_directory_name = TOP_NARR_DIRECTORY_NAME + ''
this_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=this_directory_name,
field_name=NARR_FIELD_NAMES[j],
pressure_level_mb=PRESSURE_LEVEL_MB,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
narr_matrix_by_field[j] = processed_narr_io.read_fields_from_file(
this_file_name)[0][0, ...]
narr_matrix_by_field[j] = utils.fill_nans(narr_matrix_by_field[j])
if NARR_FIELD_NAMES[j] == processed_narr_io.WET_BULB_THETA_NAME:
narr_matrix_by_field[j] = (narr_matrix_by_field[j] -
ZERO_CELSIUS_IN_KELVINS)
# (_, front_centroid_latitude_deg, front_centroid_longitude_deg
# ) = _find_nearest_front(
# front_line_table=front_line_table,
# query_latitude_deg=APPROX_FRONT_LATITUDE_DEG,
# query_longitude_deg=APPROX_FRONT_LONGITUDE_DEG)
front_centroid_latitude_deg = APPROX_FRONT_LATITUDE_DEG + 0.
front_centroid_longitude_deg = APPROX_FRONT_LONGITUDE_DEG + 0.
projection_object = nwp_model_utils.init_model_projection(
nwp_model_utils.NARR_MODEL_NAME)
these_x_metres, these_y_metres = nwp_model_utils.project_latlng_to_xy(
latitudes_deg=numpy.array([front_centroid_latitude_deg]),
longitudes_deg=numpy.array([front_centroid_longitude_deg]),
projection_object=projection_object,
model_name=nwp_model_utils.NARR_MODEL_NAME)
front_centroid_x_metres = these_x_metres[0]
front_centroid_y_metres = these_y_metres[0]
grid_spacing_metres, _ = nwp_model_utils.get_xy_grid_spacing(
model_name=nwp_model_utils.NARR_MODEL_NAME)
center_narr_row_index = int(
numpy.round(front_centroid_y_metres / grid_spacing_metres))
center_narr_column_index = int(
numpy.round(front_centroid_x_metres / grid_spacing_metres))
first_narr_row_index = center_narr_row_index - NUM_ROWS_IN_HALF_GRID
last_narr_row_index = center_narr_row_index + NUM_ROWS_IN_HALF_GRID
first_narr_column_index = (center_narr_column_index -
NUM_COLUMNS_IN_HALF_GRID)
last_narr_column_index = center_narr_column_index + NUM_COLUMNS_IN_HALF_GRID
for j in range(num_narr_fields):
narr_matrix_by_field[j] = narr_matrix_by_field[j][
first_narr_row_index:(last_narr_row_index + 1),
first_narr_column_index:(last_narr_column_index + 1)]
_, axes_object, basemap_object = nwp_plotting.init_basemap(
model_name=nwp_model_utils.NARR_MODEL_NAME,
first_row_in_full_grid=first_narr_row_index,
last_row_in_full_grid=last_narr_row_index,
first_column_in_full_grid=first_narr_column_index,
last_column_in_full_grid=last_narr_column_index,
resolution_string='i')
plotting_utils.plot_coastlines(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_countries(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_states_and_provinces(basemap_object=basemap_object,
axes_object=axes_object,
line_colour=BORDER_COLOUR)
plotting_utils.plot_parallels(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lat_deg=-90.,
upper_right_lat_deg=90.,
parallel_spacing_deg=PARALLEL_SPACING_DEG)
plotting_utils.plot_meridians(basemap_object=basemap_object,
axes_object=axes_object,
bottom_left_lng_deg=0.,
upper_right_lng_deg=360.,
meridian_spacing_deg=MERIDIAN_SPACING_DEG)
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
continue
min_colour_value = numpy.percentile(narr_matrix_by_field[j],
MIN_COLOUR_PERCENTILE)
max_colour_value = numpy.percentile(narr_matrix_by_field[j],
MAX_COLOUR_PERCENTILE)
nwp_plotting.plot_subgrid(
field_matrix=narr_matrix_by_field[j],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=THERMAL_COLOUR_MAP_OBJECT,
min_value_in_colour_map=min_colour_value,
max_value_in_colour_map=max_colour_value,
first_row_in_full_grid=first_narr_row_index,
first_column_in_full_grid=first_narr_column_index)
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=narr_matrix_by_field[j],
colour_map=THERMAL_COLOUR_MAP_OBJECT,
colour_min=min_colour_value,
colour_max=max_colour_value,
orientation='horizontal',
extend_min=True,
extend_max=True)
u_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.U_WIND_EARTH_RELATIVE_NAME)
v_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.V_WIND_EARTH_RELATIVE_NAME)
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=narr_matrix_by_field[u_wind_index],
v_wind_matrix_m_s01=narr_matrix_by_field[v_wind_index],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_full_grid=first_narr_row_index,
first_column_in_full_grid=first_narr_column_index,
plot_every_k_rows=PLOT_EVERY_KTH_WIND_BARB,
plot_every_k_columns=PLOT_EVERY_KTH_WIND_BARB,
barb_length=WIND_BARB_LENGTH,
empty_barb_radius=EMPTY_WIND_BARB_RADIUS,
colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT,
colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT)
num_fronts = len(front_line_table.index)
for i in range(num_fronts):
this_front_type_string = front_line_table[
front_utils.FRONT_TYPE_COLUMN].values[i]
if this_front_type_string == front_utils.WARM_FRONT_STRING_ID:
this_colour = WARM_FRONT_COLOUR
else:
this_colour = COLD_FRONT_COLOUR
# front_plotting.plot_polyline(
# latitudes_deg=front_line_table[
# front_utils.LATITUDES_COLUMN].values[i],
# longitudes_deg=front_line_table[
# front_utils.LONGITUDES_COLUMN].values[i],
# basemap_object=basemap_object, axes_object=axes_object,
# front_type=front_line_table[
# front_utils.FRONT_TYPE_COLUMN].values[i],
# line_width=FRONT_LINE_WIDTH, line_colour=this_colour)
print 'Saving figure to: "{0:s}"...'.format(OUTPUT_FILE_NAME)
file_system_utils.mkdir_recursive_if_necessary(file_name=OUTPUT_FILE_NAME)
pyplot.savefig(OUTPUT_FILE_NAME, dpi=OUTPUT_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=OUTPUT_FILE_NAME,
output_file_name=OUTPUT_FILE_NAME)
def _plot_one_time(valid_time_string, pressure_level_mb, title_string,
annotation_string, narr_rotation_cos_matrix,
narr_rotation_sin_matrix):
"""Plots WPC fronts and NARR fields at one time.
M = number of grid rows in the full NARR
N = number of grid columns in the full NARR
:param valid_time_string: Valid time (format "yyyy-mm-dd-HH").
:param pressure_level_mb: Pressure level (millibars).
:param title_string: Title (will be placed above figure).
:param annotation_string: Annotation (will be placed above and left of
figure).
:param narr_rotation_cos_matrix: M-by-N numpy array of cosines for wind-
rotation angles.
:param narr_rotation_sin_matrix: M-by-N numpy array of sines for wind-
rotation angles.
"""
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))
valid_time_unix_sec = time_conversion.string_to_unix_sec(
valid_time_string, DEFAULT_TIME_FORMAT)
front_file_name = fronts_io.find_file_for_one_time(
top_directory_name=TOP_FRONT_DIR_NAME,
file_type=fronts_io.POLYLINE_FILE_TYPE,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(front_file_name)
front_line_table = fronts_io.read_polylines_from_file(front_file_name)
num_narr_fields = len(NARR_FIELD_NAMES)
narr_matrix_by_field = [numpy.array([])] * num_narr_fields
for j in range(num_narr_fields):
this_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=TOP_NARR_DIRECTORY_NAME,
field_name=NARR_FIELD_NAMES[j],
pressure_level_mb=pressure_level_mb,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
narr_matrix_by_field[j] = processed_narr_io.read_fields_from_file(
this_file_name)[0][0, ...]
narr_matrix_by_field[j] = utils.fill_nans(narr_matrix_by_field[j])
narr_matrix_by_field[j] = narr_matrix_by_field[j][narr_row_limits[0]:(
narr_row_limits[1] +
1), narr_column_limits[0]:(narr_column_limits[1] + 1)]
if NARR_FIELD_NAMES[j] == processed_narr_io.WET_BULB_THETA_NAME:
narr_matrix_by_field[j] = (narr_matrix_by_field[j] -
ZERO_CELSIUS_IN_KELVINS)
_, 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)
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
continue
min_colour_value = numpy.percentile(narr_matrix_by_field[j],
MIN_COLOUR_PERCENTILE)
max_colour_value = numpy.percentile(narr_matrix_by_field[j],
MAX_COLOUR_PERCENTILE)
nwp_plotting.plot_subgrid(
field_matrix=narr_matrix_by_field[j],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
colour_map=THERMAL_COLOUR_MAP_OBJECT,
min_value_in_colour_map=min_colour_value,
max_value_in_colour_map=max_colour_value,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0])
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=narr_matrix_by_field[j],
colour_map=THERMAL_COLOUR_MAP_OBJECT,
colour_min=min_colour_value,
colour_max=max_colour_value,
orientation='horizontal',
extend_min=True,
extend_max=True,
fraction_of_axis_length=0.9)
this_cos_matrix = narr_rotation_cos_matrix[narr_row_limits[0]:(
narr_row_limits[1] + 1), narr_column_limits[0]:(narr_column_limits[1] +
1)]
this_sin_matrix = narr_rotation_sin_matrix[narr_row_limits[0]:(
narr_row_limits[1] + 1), narr_column_limits[0]:(narr_column_limits[1] +
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)
narr_matrix_by_field[u_wind_index], narr_matrix_by_field[v_wind_index] = (
nwp_model_utils.rotate_winds_to_earth_relative(
u_winds_grid_relative_m_s01=narr_matrix_by_field[u_wind_index],
v_winds_grid_relative_m_s01=narr_matrix_by_field[v_wind_index],
rotation_angle_cosines=this_cos_matrix,
rotation_angle_sines=this_sin_matrix))
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=narr_matrix_by_field[u_wind_index],
v_wind_matrix_m_s01=narr_matrix_by_field[v_wind_index],
model_name=nwp_model_utils.NARR_MODEL_NAME,
axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_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=False,
colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT,
colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT)
num_fronts = len(front_line_table.index)
for i in range(num_fronts):
this_front_type_string = front_line_table[
front_utils.FRONT_TYPE_COLUMN].values[i]
if this_front_type_string == front_utils.WARM_FRONT_STRING_ID:
this_colour = WARM_FRONT_COLOUR
else:
this_colour = COLD_FRONT_COLOUR
front_plotting.plot_front_with_markers(
line_latitudes_deg=front_line_table[
front_utils.LATITUDES_COLUMN].values[i],
line_longitudes_deg=front_line_table[
front_utils.LONGITUDES_COLUMN].values[i],
axes_object=axes_object,
basemap_object=basemap_object,
front_type_string=front_line_table[
front_utils.FRONT_TYPE_COLUMN].values[i],
marker_colour=this_colour)
pyplot.title(title_string)
plotting_utils.annotate_axes(axes_object=axes_object,
annotation_string=annotation_string)
file_system_utils.mkdir_recursive_if_necessary(
directory_name=OUTPUT_DIR_NAME)
figure_file_name = '{0:s}/fronts_{1:04d}mb_{2:s}.jpg'.format(
OUTPUT_DIR_NAME, pressure_level_mb, valid_time_string)
print 'Saving figure to: "{0:s}"...'.format(figure_file_name)
pyplot.savefig(figure_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=figure_file_name,
output_file_name=figure_file_name)
return figure_file_name
def _plot_observations_one_time(
valid_time_string, title_string, annotation_string, output_file_name):
"""Plots observations (NARR predictors and WPC fronts) for one valid time.
:param valid_time_string: Valid time (format "yyyy-mm-dd-HH").
:param title_string: Title (will be placed above figure).
:param annotation_string: Text annotation (will be placed in top left of
figure).
:param output_file_name: Path to output file (figure will be saved here).
"""
(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)
valid_time_unix_sec = time_conversion.string_to_unix_sec(
valid_time_string, INPUT_TIME_FORMAT)
front_file_name = fronts_io.find_file_for_one_time(
top_directory_name=TOP_FRONT_DIR_NAME,
file_type=fronts_io.POLYLINE_FILE_TYPE,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(front_file_name)
front_line_table = fronts_io.read_polylines_from_file(front_file_name)
num_narr_fields = len(NARR_FIELD_NAMES)
narr_matrix_by_field = [numpy.array([])] * num_narr_fields
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
this_directory_name = TOP_NARR_WIND_DIR_NAME + ''
else:
this_directory_name = TOP_NARR_DIR_NAME + ''
this_file_name = processed_narr_io.find_file_for_one_time(
top_directory_name=this_directory_name,
field_name=NARR_FIELD_NAMES[j], pressure_level_mb=PRESSURE_LEVEL_MB,
valid_time_unix_sec=valid_time_unix_sec)
print 'Reading data from: "{0:s}"...'.format(this_file_name)
narr_matrix_by_field[j] = processed_narr_io.read_fields_from_file(
this_file_name)[0][0, ...]
narr_matrix_by_field[j] = utils.fill_nans(narr_matrix_by_field[j])
narr_matrix_by_field[j] = narr_matrix_by_field[j][
narr_row_limits[0]:(narr_row_limits[1] + 1),
narr_column_limits[0]:(narr_column_limits[1] + 1)
]
if NARR_FIELD_NAMES[j] == processed_narr_io.WET_BULB_THETA_NAME:
narr_matrix_by_field[j] = (
narr_matrix_by_field[j] - ZERO_CELSIUS_IN_KELVINS
)
_, 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)
for j in range(num_narr_fields):
if NARR_FIELD_NAMES[j] in WIND_FIELD_NAMES:
continue
min_colour_value = numpy.percentile(
narr_matrix_by_field[j], MIN_COLOUR_PERCENTILE)
max_colour_value = numpy.percentile(
narr_matrix_by_field[j], MAX_COLOUR_PERCENTILE)
nwp_plotting.plot_subgrid(
field_matrix=narr_matrix_by_field[j],
model_name=nwp_model_utils.NARR_MODEL_NAME, axes_object=axes_object,
basemap_object=basemap_object, colour_map=THERMAL_COLOUR_MAP_OBJECT,
min_value_in_colour_map=min_colour_value,
max_value_in_colour_map=max_colour_value,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_column_limits[0])
plotting_utils.add_linear_colour_bar(
axes_object_or_list=axes_object,
values_to_colour=narr_matrix_by_field[j],
colour_map=THERMAL_COLOUR_MAP_OBJECT, colour_min=min_colour_value,
colour_max=max_colour_value, orientation='vertical',
extend_min=True, extend_max=True,
fraction_of_axis_length=LENGTH_FRACTION_FOR_THETA_COLOUR_BAR)
u_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.U_WIND_EARTH_RELATIVE_NAME)
v_wind_index = NARR_FIELD_NAMES.index(
processed_narr_io.V_WIND_EARTH_RELATIVE_NAME)
nwp_plotting.plot_wind_barbs_on_subgrid(
u_wind_matrix_m_s01=narr_matrix_by_field[u_wind_index],
v_wind_matrix_m_s01=narr_matrix_by_field[v_wind_index],
model_name=nwp_model_utils.NARR_MODEL_NAME, axes_object=axes_object,
basemap_object=basemap_object,
first_row_in_full_grid=narr_row_limits[0],
first_column_in_full_grid=narr_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,
colour_map=WIND_COLOUR_MAP_OBJECT,
colour_minimum_kt=MIN_COLOUR_WIND_SPEED_KT,
colour_maximum_kt=MAX_COLOUR_WIND_SPEED_KT)
num_fronts = len(front_line_table.index)
for i in range(num_fronts):
this_front_type_string = front_line_table[
front_utils.FRONT_TYPE_COLUMN].values[i]
if this_front_type_string == front_utils.WARM_FRONT_STRING_ID:
this_colour = WARM_FRONT_COLOUR
else:
this_colour = COLD_FRONT_COLOUR
front_plotting.plot_polyline(
latitudes_deg=front_line_table[
front_utils.LATITUDES_COLUMN].values[i],
longitudes_deg=front_line_table[
front_utils.LONGITUDES_COLUMN].values[i],
basemap_object=basemap_object, axes_object=axes_object,
front_type=front_line_table[
front_utils.FRONT_TYPE_COLUMN].values[i],
line_width=FRONT_LINE_WIDTH, line_colour=this_colour)
pyplot.title(title_string)
plotting_utils.annotate_axes(
axes_object=axes_object, annotation_string=annotation_string)
print 'Saving figure to: "{0:s}"...'.format(output_file_name)
file_system_utils.mkdir_recursive_if_necessary(file_name=output_file_name)
pyplot.savefig(output_file_name, dpi=FIGURE_RESOLUTION_DPI)
pyplot.close()
imagemagick_utils.trim_whitespace(input_file_name=output_file_name,
output_file_name=output_file_name)
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 _run():
"""Creates histograms of warm-front and cold-front lengths.
This is effectively the main method.
"""
input_file_names = glob.glob(INPUT_FILE_PATTERN)
num_files = len(input_file_names)
list_of_front_line_tables = [pandas.DataFrame()] * num_files
for i in range(num_files):
print 'Reading data from: "{0:s}"...'.format(input_file_names[i])
list_of_front_line_tables[i] = fronts_io.read_polylines_from_file(
input_file_names[i])
if i == 0:
continue
list_of_front_line_tables[i] = list_of_front_line_tables[i].align(
list_of_front_line_tables[0], axis=1)[0]
print SEPARATOR_STRING
front_line_table = pandas.concat(list_of_front_line_tables,
axis=0,
ignore_index=True)
front_line_table = _project_fronts_latlng_to_narr(front_line_table)
print SEPARATOR_STRING
front_lengths_metres = _get_front_lengths(front_line_table)
print SEPARATOR_STRING
nan_flags = numpy.isnan(front_lengths_metres)
warm_front_flags = numpy.array([
s == front_utils.WARM_FRONT_STRING_ID
for s in front_line_table[front_utils.FRONT_TYPE_COLUMN].values
])
cold_front_flags = numpy.array([
s == front_utils.COLD_FRONT_STRING_ID
for s in front_line_table[front_utils.FRONT_TYPE_COLUMN].values
])
warm_front_indices = numpy.where(
numpy.logical_and(warm_front_flags, numpy.invert(nan_flags)))[0]
cold_front_indices = numpy.where(
numpy.logical_and(cold_front_flags, numpy.invert(nan_flags)))[0]
warm_front_lengths_metres = front_lengths_metres[warm_front_indices]
cold_front_lengths_metres = front_lengths_metres[cold_front_indices]
print(
'Number of fronts = {0:d} ... warm fronts with defined length = {1:d} '
'... cold fronts with defined length = {2:d}').format(
len(front_lengths_metres), len(warm_front_lengths_metres),
len(cold_front_lengths_metres))
_, num_warm_fronts_by_bin = histograms.create_histogram(
input_values=warm_front_lengths_metres,
num_bins=NUM_BINS,
min_value=MIN_HISTOGRAM_LENGTH_METRES,
max_value=MAX_HISTOGRAM_LENGTH_METRES)
print 'Sum of bin counts for warm fronts = {0:d}'.format(
numpy.sum(num_warm_fronts_by_bin))
_plot_histogram(num_fronts_by_bin=num_warm_fronts_by_bin,
front_type_string=front_utils.WARM_FRONT_STRING_ID,
title_string='Warm fronts',
annotation_string='(a)',
output_file_name=WARM_FRONT_FILE_NAME)
_, num_cold_fronts_by_bin = histograms.create_histogram(
input_values=cold_front_lengths_metres,
num_bins=NUM_BINS,
min_value=MIN_HISTOGRAM_LENGTH_METRES,
max_value=MAX_HISTOGRAM_LENGTH_METRES)
print 'Sum of bin counts for cold fronts = {0:d}'.format(
numpy.sum(num_cold_fronts_by_bin))
_plot_histogram(num_fronts_by_bin=num_cold_fronts_by_bin,
front_type_string=front_utils.COLD_FRONT_STRING_ID,
title_string='Cold fronts',
annotation_string='(b)',
output_file_name=COLD_FRONT_FILE_NAME)
print 'Concatenating figures to: "{0:s}"...'.format(CONCAT_FILE_NAME)
imagemagick_utils.concatenate_images(
input_file_names=[WARM_FRONT_FILE_NAME, COLD_FRONT_FILE_NAME],
output_file_name=CONCAT_FILE_NAME,
num_panel_rows=1,
num_panel_columns=2,
output_size_pixels=OUTPUT_SIZE_PIXELS)