def output_plot_variability_period_time(modf, info, output_path):
elements = []
# Section title
default_title = _('Plot variability')
# Required values
location = info['location_metocean']
driver = info['name_driver']
descriptor = info['name_descriptor']
block = info['name_block']
section = info['name_section']
variability = info['variability_section']
initial_date = info['initial_date_section']
final_date = info['final_date_section']
# Optional values and default values
title = get_key(info, 'title_section', default_title)
var_name = get_key(info, 'var_name_descriptor', descriptor)
var_unit = get_key(info, 'unit_descriptor', '')
circular = get_key(info, 'circular_descriptor', False)
x_step = get_key(info, 'x_step_section', 1)
# Input tex section
input_tex(elements, info, output_path, section)
# Computation
data = extract_data(modf, descriptor_name=descriptor)
mask = ((data.index > initial_date) & (data.index < final_date))
data_crop = data[mask]
# Figure
kind = 'figure'
default_caption = _('Variability boxplot') + ': {}'.format(
info['title_descriptor'])
caption = get_key(info, 'variability_figure_caption_section',
default_caption)
path = get_output_name(location=location,
driver=driver,
name=descriptor,
block=block,
title=section,
kind=kind)
summary.plot_variability(data_crop,
frecuency=variability,
title='',
var_name=var_name,
var_unit=var_unit,
fig_filename=os.path.join(output_path, path),
circular=circular,
x_step=x_step)
elements.append([path, kind, caption])
return pd.DataFrame(elements, columns=['path', 'kind', 'caption']), title
def output_plot_histogram(modf, info, output_path):
elements = []
# Section title
default_title = _('Plot histogram')
# Required values
location = info['location_metocean']
driver = info['name_driver']
descriptor = info['name_descriptor']
block = info['name_block']
section = info['name_section']
# Optional values and default values
title = get_key(info, 'title_section', default_title)
var_name = get_key(info, 'var_name_descriptor', descriptor)
var_unit = get_key(info, 'unit_descriptor', '')
circular = get_key(info, 'circular_descriptor', False)
kernel = get_key(info, 'kernel_section', False)
bins = get_key(info, 'bins_section', 'auto')
# Input tex section
input_tex(elements, info, output_path, section)
# Computation
data = extract_data(modf, descriptor_name=descriptor)
# Figure
kind = 'figure'
default_caption = _('Histogram plot') + ': {}'.format(
info['title_descriptor'])
caption = get_key(info, 'histogram_figure_caption_section',
default_caption)
path = get_output_name(location=location,
driver=driver,
name=descriptor,
block=block,
title=section,
kind=kind)
summary.plot_histogram(data,
title='',
var_name=var_name,
var_unit=var_unit,
fig_filename=os.path.join(output_path, path),
circular=circular,
kernel=kernel,
bins=bins)
elements.append([path, kind, caption])
return pd.DataFrame(elements, columns=['path', 'kind', 'caption']), title
def output_table_summary(modf, info, output_path):
elements = []
# Section title
default_title = _('Summary table')
# Required values
location = info['location_metocean']
driver = info['name_driver']
descriptor = info['name_descriptor']
block = info['name_block']
section = info['name_section']
# Optional values and default values
title = get_key(info, 'title_section', default_title)
# Input tex section
input_tex(elements, info, output_path, section)
# Computation
data = extract_data(modf, descriptor_name=descriptor)
# Table
kind = 'table'
default_caption = _('Summary table') + ': {}'.format(
info['title_descriptor'])
caption = get_key(info, 'summary_table_caption_section', default_caption)
path = get_output_name(location=location,
driver=driver,
name=descriptor,
block=block,
title=section,
kind=kind)
data_summary = summary.get_summary(data)
save_table(data_summary, os.path.join(output_path, path))
elements.append([os.path.join(output_path, path), kind, caption])
return pd.DataFrame(elements, columns=['path', 'kind', 'caption']), title
def output_plot_empirical_cdf(modf, info, output_path):
elements = []
# Section title
default_title = _('Plot empirical CDF')
# Required values
location = info['location_metocean']
driver = info['name_driver']
descriptor = info['name_descriptor']
block = info['name_block']
section = info['name_section']
# Optional values and default values
title = get_key(info, 'title_section', default_title)
var_name = get_key(info, 'var_name_descriptor', descriptor)
var_unit = get_key(info, 'unit_descriptor', '')
circular = get_key(info, 'circular_descriptor', False)
label_empirical = get_key(info, 'label_empirical_section', '')
label_kernel = get_key(info, 'label_kernel_section', '')
bins = get_key(info, 'bins_section', 100)
# Input tex section
input_tex(elements, info, output_path, section)
# Computation
data = extract_data(modf, descriptor_name=descriptor)
cumulative = True
data_empirical = empirical_distributions.ecdf_histogram(data)
data_kernel = empirical_distributions.kde_sm(data,
cumulative=cumulative,
gridsize=bins)
# Figure
kind = 'figure'
default_caption = _('Empirical CDF') + ': {}'.format(
info['title_descriptor'])
caption = get_key(info, 'empirical_pdf_figure_caption_section',
default_caption)
path = get_output_name(location=location,
driver=driver,
name=descriptor,
block=block,
title=section,
kind=kind)
empirical_distributions.plot_kde(data_empirical,
data_kernel,
cumulative,
title='',
var_name=var_name,
var_unit=var_unit,
fig_filename=os.path.join(
output_path, path),
circular=circular,
label_empirical=label_empirical,
label_kernel=label_kernel)
elements.append([path, kind, caption])
return pd.DataFrame(elements, columns=['path', 'kind', 'caption']), title
def output_missing_values(modf, info, output_path):
elements = []
# Section title
default_title = _('Missing values')
# Required values
location = info['location_metocean']
driver = info['name_driver']
descriptor = info['name_descriptor']
block = info['name_block']
section = info['name_section']
# Optional values and default values
title = get_key(info, 'title_section', default_title)
var_name = get_key(info, 'var_name_descriptor', descriptor)
var_unit = get_key(info, 'unit_descriptor', '')
circular = get_key(info, 'circular_descriptor', False)
# Input tex section
input_tex(elements, info, output_path, section)
# Computation
data = extract_data(modf, descriptor_name=descriptor)
time_step = missing_values.find_timestep(data)
data_gaps = missing_values.find_missing_values(data, time_step)
# Figure
kind = 'figure'
default_caption = _('Missing values plot') + ': {}'.format(
info['title_descriptor'])
caption = get_key(info, 'missing_values_figure_caption_section',
default_caption)
path = get_output_name(location=location,
driver=driver,
name=descriptor,
block=block,
title=section,
kind=kind)
missing_values.plot_missing_values(data=data,
data_gaps=data_gaps,
title='',
var_name=var_name,
var_unit=var_unit,
fig_filename=os.path.join(
output_path, path),
circular=circular,
label=var_name)
elements.append([path, kind, caption])
# Table
kind = 'table'
default_caption = _('Missing values table') + ': {}'.format(
info['title_descriptor'])
caption = get_key(info, 'missing_values_table_caption_section',
default_caption)
path = get_output_name(location=location,
driver=driver,
name=descriptor,
block=block,
title=section,
kind=kind)
data_gaps_report = missing_values.missing_values_report(data, data_gaps)
save_table(data_gaps_report, os.path.join(output_path, path))
elements.append([os.path.join(output_path, path), kind, caption])
return pd.DataFrame(elements, columns=['path', 'kind', 'caption']), title