def create_target_diagram(statistics, config=None):
figure = plt.figure()
if config is None:
config = get_default_config()
statistics = utils.ensure_list(statistics)
diagram = TargetDiagram(figure, config.normalise_target_diagram,
config.show_legends,
config.utilise_stddev_difference)
diagram.setup_axes()
for stats in statistics:
diagram.plot_sample(
stats['bias'], stats['unbiased_rmse'], stats['normalised_rmse'],
stats['rmse'], stats['ref_stddev'], stats['stddev'],
create_sample_name(stats['model_name'], stats['unit']))
if config.normalise_target_diagram:
diagram.plot_correcoeff_marker_line()
diagram.update_legend()
if not config.normalise_target_diagram:
diagram.update_ranges(config.target_diagram_bounds)
return diagram
def create_taylor_diagrams(statistics, config=None):
if config is None:
config = get_default_config()
statistics = utils.ensure_list(statistics)
statistics_by_name_and_unit = sort_statistics_by_name_and_unit(
config, statistics)
diagrams = []
for name in statistics_by_name_and_unit.keys():
for current_unit in statistics_by_name_and_unit[name].keys():
current_statistics = statistics_by_name_and_unit[name][
current_unit]
ref_names = list(
map(lambda x: x.get('ref_name'), current_statistics))
ref_stddevs = list(
map(lambda x: x.get('ref_stddev'), current_statistics)
) if config.use_absolute_standard_deviation else list(
np.ones(len(current_statistics)))
units = list(map(lambda x: x.get('unit'), current_statistics))
ref = tuple(zip(ref_names, ref_stddevs, units))
model_stddevs = list(
map(lambda x: x.get('stddev'), current_statistics)
) if config.use_absolute_standard_deviation else list(
map(lambda x: x.get('normalised_stddev'), current_statistics))
max_stddev = max(ref_stddevs + model_stddevs) * 1.5
for v in ref_stddevs:
if v == 0.0 or np.isnan(v):
logging.warning(
'Unable to create Taylor diagram from statistics.')
logging.debug('Statistics: %s' % current_statistics)
continue
figure = plt.figure()
diagram = TaylorDiagram(figure, ref,
config.show_negative_corrcoeff,
config.use_absolute_standard_deviation,
config.show_legends, max_stddev)
diagram.setup_axes()
for stats in current_statistics:
model_name = stats[
'model_name'] if 'model_name' in stats else None
stddev = stats[
'stddev'] if config.use_absolute_standard_deviation else stats[
'normalised_stddev']
diagram.plot_sample(stats['corrcoeff'], stddev, model_name,
stats['unit'])
diagram.update_legend()
diagrams.append(diagram)
return diagrams
def create_target_diagram(statistics, config=None):
figure = plt.figure()
if config is None:
config = get_default_config()
statistics = utils.ensure_list(statistics)
diagram = TargetDiagram(figure, config.normalise_target_diagram, config.show_legends, config.utilise_stddev_difference)
diagram.setup_axes()
for stats in statistics:
diagram.plot_sample(stats['bias'], stats['unbiased_rmse'], stats['normalised_rmse'], stats['rmse'],
stats['ref_stddev'], stats['stddev'], create_sample_name(stats['model_name'], stats['unit']))
if config.normalise_target_diagram:
diagram.plot_correcoeff_marker_line()
diagram.update_legend()
if not config.normalise_target_diagram:
diagram.update_ranges(config.target_diagram_bounds)
return diagram
def create_taylor_diagrams(statistics, config=None):
if config is None:
config = get_default_config()
statistics = utils.ensure_list(statistics)
statistics_by_name_and_unit = sort_statistics_by_name_and_unit(config, statistics)
diagrams = []
for name in statistics_by_name_and_unit.keys():
for current_unit in statistics_by_name_and_unit[name].keys():
current_statistics = statistics_by_name_and_unit[name][current_unit]
ref_names = list(map(lambda x: x.get('ref_name'), current_statistics))
ref_stddevs = list(map(lambda x: x.get('ref_stddev'), current_statistics)) if config.use_absolute_standard_deviation else list(np.ones(len(current_statistics)))
units = list(map(lambda x: x.get('unit'), current_statistics))
ref = tuple(zip(ref_names, ref_stddevs, units))
model_stddevs = list(map(lambda x: x.get('stddev'), current_statistics)) if config.use_absolute_standard_deviation else list(map(lambda x: x.get('normalised_stddev'), current_statistics))
max_stddev = max(ref_stddevs + model_stddevs) * 1.5
for v in ref_stddevs:
if v == 0.0 or np.isnan(v):
logging.warning('Unable to create Taylor diagram from statistics.')
logging.debug('Statistics: %s' % current_statistics)
continue
figure = plt.figure()
diagram = TaylorDiagram(figure, ref, config.show_negative_corrcoeff, config.use_absolute_standard_deviation, config.show_legends, max_stddev)
diagram.setup_axes()
for stats in current_statistics:
model_name = stats['model_name'] if 'model_name' in stats else None
stddev = stats['stddev'] if config.use_absolute_standard_deviation else stats['normalised_stddev']
diagram.plot_sample(stats['corrcoeff'], stddev, model_name, stats['unit'])
diagram.update_legend()
diagrams.append(diagram)
return diagrams
def xhtml(self,
statistics_list,
matchup_count,
matchups,
data,
target_file,
taylor_target_files=None,
target_diagram_file=None,
density_plot_files=None):
filename = os.path.dirname(os.path.realpath(
__file__)) + '/../resources/matchup_report_template.xml'
template = Template(filename=filename)
buf = StringIO()
taylor_target_files = utils.ensure_list(taylor_target_files)
density_plot_files = utils.ensure_list(density_plot_files)
all_relative_stats = []
all_model_stats = []
all_ref_stats = []
for stats in statistics_list:
pair_statistics = {}
for key in ('rmse', 'unbiased_rmse', 'normalised_rmse', 'bias',
'pbias', 'corrcoeff', 'reliability_index',
'model_efficiency', 'normalised_stddev'):
pair_statistics[key] = format_statistic(stats, key)
pair = (stats['model_name'], stats['ref_name'], pair_statistics)
all_relative_stats.append(pair)
model_pair = {}
for key in ('min', 'max', 'mean', 'stddev', 'median', 'p90',
'p95'):
model_pair[key] = format_statistic(stats, key)
all_model_stats.append((stats['model_name'], model_pair))
ref_pair = {}
for key in ('ref_min', 'ref_max', 'ref_mean', 'ref_stddev',
'ref_median', 'ref_p90', 'ref_p95'):
ref_pair[key.replace('ref_',
'')] = format_statistic(stats, key)
all_ref_stats.append((stats['ref_name'], ref_pair))
density_plot_files = get_basenames(density_plot_files)
taylor_target_files = get_basenames(taylor_target_files)
target_diagram_file = os.path.basename(
target_diagram_file) if target_diagram_file is not None else None
ctx = Context(
buf,
pairs=all_relative_stats,
performed_at=datetime.now().strftime('%b %d, %Y at %H:%M:%S'),
record_count=matchup_count,
time_delta=self.config.time_delta,
depth_delta=self.config.depth_delta,
ddof=self.config.ddof,
alpha=self.config.alpha,
beta=self.config.beta,
all_relative_stats=all_relative_stats,
all_model_stats=all_model_stats,
all_ref_stats=all_ref_stats,
matchups=matchups,
data=data,
reference_vars=data.ref_vars(),
model_vars=data.model_vars(),
write_taylor_diagrams=self.config.write_taylor_diagrams,
taylor_target_files=taylor_target_files,
write_target_diagram=self.config.write_target_diagram,
target_diagram_file=target_diagram_file,
density_plot_files=density_plot_files)
template.render_context(ctx)
xml = buf.getvalue()
directory = os.path.dirname(target_file)
if not os.path.exists(directory):
os.makedirs(directory)
file = open(target_file, 'w')
file.write(xml)
file.close()
def xhtml(self, statistics_list, matchup_count, matchups, data, target_file, taylor_target_files=None, target_diagram_file=None, density_plot_files=None):
filename = os.path.dirname(os.path.realpath(__file__)) + '/../resources/matchup_report_template.xml'
template = Template(filename=filename)
buf = StringIO()
taylor_target_files = utils.ensure_list(taylor_target_files)
density_plot_files = utils.ensure_list(density_plot_files)
all_relative_stats = []
all_model_stats = []
all_ref_stats = []
for stats in statistics_list:
pair_statistics = {}
for key in ('rmse', 'unbiased_rmse', 'normalised_rmse', 'bias', 'pbias', 'corrcoeff', 'reliability_index', 'model_efficiency', 'normalised_stddev'):
pair_statistics[key] = format_statistic(stats, key)
pair = (stats['model_name'], stats['ref_name'], pair_statistics)
all_relative_stats.append(pair)
model_pair = {}
for key in ('min', 'max', 'mean', 'stddev', 'median', 'p90', 'p95'):
model_pair[key] = format_statistic(stats, key)
all_model_stats.append((stats['model_name'], model_pair))
ref_pair = {}
for key in ('ref_min', 'ref_max', 'ref_mean', 'ref_stddev', 'ref_median', 'ref_p90', 'ref_p95'):
ref_pair[key.replace('ref_', '')] = format_statistic(stats, key)
all_ref_stats.append((stats['ref_name'], ref_pair))
density_plot_files = get_basenames(density_plot_files)
taylor_target_files = get_basenames(taylor_target_files)
target_diagram_file = os.path.basename(target_diagram_file) if target_diagram_file is not None else None
ctx = Context(buf,
pairs=all_relative_stats,
performed_at=datetime.now().strftime('%b %d, %Y at %H:%M:%S'),
record_count=matchup_count,
time_delta=self.config.time_delta,
depth_delta=self.config.depth_delta,
ddof=self.config.ddof,
alpha=self.config.alpha,
beta=self.config.beta,
all_relative_stats=all_relative_stats,
all_model_stats=all_model_stats,
all_ref_stats=all_ref_stats,
matchups=matchups,
data=data,
reference_vars=data.ref_vars(),
model_vars=data.model_vars(),
write_taylor_diagrams=self.config.write_taylor_diagrams,
taylor_target_files=taylor_target_files,
write_target_diagram=self.config.write_target_diagram,
target_diagram_file=target_diagram_file,
density_plot_files=density_plot_files)
template.render_context(ctx)
xml = buf.getvalue()
directory = os.path.dirname(target_file)
if not os.path.exists(directory):
os.makedirs(directory)
file = open(target_file, 'w')
file.write(xml)
file.close()
