def plot_missing_values(data, data_gaps, data_column='', title='', var_name='', var_unit='', fig_filename='',
circular=False, label='observations'):
# Get style
plot.get_default_plot_style()
# Get values
values = plot.get_values(data, data_column)
# Get var label
var_label = plot.get_var_label(var_name, var_unit)
# Plot
ax = plt.axes()
ax.plot(values.index, values, ':', alpha=0.3, label=label)
ax.plot(values.loc[data_gaps.loc[:, 'pos_ini']], '.k', markersize=10, label='Gaps_initial_postion')
ax.plot(values.loc[data_gaps.loc[:, 'pos_fin']], '.r', markersize=10, label='Gaps_final_postion')
ax.legend(loc='upper right', facecolor='white')
plot.plot_title(ax, title)
if circular is True:
circular_labels, circular_ticks = plot.get_circular_ticks()
plt.yticks(circular_ticks, circular_labels)
ax.set_ylabel(var_label)
# fixing the x tick labels are all squashed together
plt.gcf().autofmt_xdate()
plot.save_figure(fig_filename)
def plot_histogram(data,
title='',
var_name='',
var_unit='',
fig_filename='',
rug=False,
data_column='',
circular=False,
kernel=False,
bins='auto'):
plot.get_default_plot_style()
values = plot.get_values(data, data_column)
var_label = plot.get_var_label(var_name, var_unit)
# Remove null values
values_clean = values.dropna(axis=0, how='all')
ax = sns.distplot(values_clean,
rug=rug,
hist_kws={'edgecolor': 'black'},
kde=kernel,
bins=bins,
norm_hist=True)
plot.plot_title(ax, title)
if circular:
circular_labels, circular_ticks = plot.get_circular_ticks()
plt.xticks(circular_ticks, circular_labels)
ax.set_xlabel(var_label)
plot.save_figure(fig_filename)
def plot_rose(data,
data_column,
dir_column,
title='',
var_name='',
var_unit='',
fig_filename='',
legend_position='upper left',
xlabels=('E', 'N-E', 'N', 'N-W', 'W', 'S-W', 'S', 'S-E')):
plot.get_default_plot_style(context='talk')
var_label = plot.get_var_label(var_name, var_unit)
ax = plot_windrose(data,
kind='bar',
var_name=data_column,
direction_name=dir_column,
normed=True,
opening=0.8,
edgecolor=n(b'white'))
plot.plot_title(ax, title)
ax.set_xlabel(var_label)
ax.legend(loc=legend_position, fontsize='small')
ax.set_xticklabels(xlabels)
plot.save_figure(fig_filename)
def peaks_plot(data,
peaks,
data_column='',
title='',
var_name='',
var_unit='',
fig_filename='',
circular=False,
data_label='',
peaks_label=''):
# Get style
plot.get_default_plot_style()
# Get values
values = plot.get_values(data, data_column)
# Get var label
var_label = plot.get_var_label(var_name, var_unit)
# Plot
ax = plt.axes()
ax.plot(values.index, values, ':', alpha=0.3, label=data_label)
# ax.plot(peaks.index, peaks, '.r', markersize=10, label=peaks_label)
ax.legend(loc='upper right', facecolor='white')
plot.plot_title(ax, title)
if circular:
circular_labels, circular_ticks = plot.get_circular_ticks()
plt.yticks(circular_ticks, circular_labels)
ax.set_ylabel(var_label)
# fixing the x tick labels are all squashed together
plt.gcf().autofmt_xdate()
plt.xlim([1959, 2018])
plot.save_figure(fig_filename)
def plot_fit_kde(x, y, data, data_name, title='', var_name='', var_unit='', kde=True, hist=False, cumulative=False,
fig_filename='', fit_label='fitting', hist_label='empirical', kde_label='kernel'):
plot.get_default_plot_style()
# kw_params is a keyworded variable length argument
kw_params = {'hist_kws': {'label': hist_label, 'edgecolor': 'black'}, 'kde_kws': {'label': kde_label}}
if cumulative:
# noinspection PyTypeChecker
kw_params['hist_kws'].update({'cumulative': True})
# noinspection PyTypeChecker
kw_params['kde_kws'].update({'cumulative': True})
var_label = plot.get_var_label(var_name, var_unit)
ax = sns.distplot(data[data_name], kde=kde, hist=hist, **kw_params)
ax.plot(x, y, label=fit_label)
ax.legend(loc='upper right', facecolor='white')
plot.plot_title(ax, title)
ax.set_xlabel(var_label)
plot.save_figure(fig_filename)
def plot_empirical(dist, fig_filename=''):
# width is in axis units
width, _, _ = plot.calculation_width(dist)
plt.bar(dist.index, dist, width, edgecolor='black', linewidth=0.5)
plot.save_figure(fig_filename)
def plot_extremal_cdf(x_extremal,
y_extremal,
ecdf_extemal,
n_sim_boot,
boot_extremal,
alpha,
title='',
var_name='',
var_unit='',
fig_filename='',
circular=False,
extremal_label='',
empirical_label=''):
# TODO: hacer paquete opcional
from tqdm import tqdm
# Get style
plot.get_default_plot_style()
# Prepare label and units
var_label = plot.get_var_label(var_name, var_unit)
ax = plt.axes()
# for sim in tqdm(range(n_sim_boot)):
# ax.plot(x_extremal, boot_extremal['y_boot'][sim], 'grey', alpha=0.35)
ax.plot(ecdf_extemal.index,
ecdf_extemal,
'.',
color='#ff7f0e',
label=empirical_label)
ax.plot(x_extremal, y_extremal, color='#1f77b4', label=extremal_label)
ax.plot(x_extremal,
boot_extremal['lower_band'],
'--',
color='#1f77b4',
label='Lower {}% confidence band'.format((1 - alpha) * 100))
ax.plot(x_extremal,
boot_extremal['upper_band'],
'--',
color='#1f77b4',
label='Upper {}% confidence band'.format((1 - alpha) * 100))
ax.legend()
plot.plot_title(ax, title)
if circular:
circular_labels, circular_ticks = plot.get_circular_ticks()
plt.yticks(circular_ticks, circular_labels)
ax.set_xlabel(var_label)
ax.set_ylabel('CDF')
# fixing the x tick labels are all squashed together
plt.gcf().autofmt_xdate()
plot.save_figure(fig_filename)
def plot_variability(data,
frecuency,
title='',
var_name='',
var_unit='',
x_step=1,
fliersize=1,
linewidth=0.5,
color='skyblue',
palette=None,
fig_filename='',
circular=False):
plot.get_default_plot_style()
var_label = plot.get_var_label(var_name, var_unit)
# gettattr return the value of the named attribute,
# i.e. data.index.daily is equivalent to getattr(data.index, 'daily')
x = getattr(data.index, frecuency)
ax = sns.boxplot(x=x,
y=data,
fliersize=fliersize,
linewidth=linewidth,
color=color,
palette=palette)
plot.plot_title(ax, title)
if circular:
circular_labels, circular_ticks = plot.get_circular_ticks()
plt.yticks(circular_ticks, circular_labels)
if frecuency == 'month':
months_labels, months_ticks = plot.get_months_ticks()
plt.xticks(months_ticks, months_labels)
else:
if x_step > 1:
x_labels, x_ticks = plot.get_x_ticks(x, ax.get_xlim(), x_step)
plt.xticks(x_ticks, x_labels)
# fixing the x tick labels are all squashed together
ax.xaxis_date()
plt.gcf().autofmt_xdate()
ax.xaxis.grid(True)
ax.set_xlabel('')
ax.set_ylabel(var_label)
plot.save_figure(fig_filename)
def plot_fit_kde_ecdf(x_gev, y_gev, x_ecdf, y_ecdf, x_ecdf_kde, y_ecdf_kde, var_name='', var_unit='', label_fit='',
label_observation='', label_kde='', title='', fig_filename=''):
plot.get_default_plot_style()
var_label = plot.get_var_label(var_name, var_unit)
ax = plt.axes()
ax.plot(x_gev, y_gev, 'k', label=label_fit)
ax.plot(x_ecdf, y_ecdf, '.b', label=label_observation)
ax.plot(x_ecdf_kde, y_ecdf_kde, 'r', label=label_kde)
ax.legend(loc='upper right', facecolor='white')
plot.plot_title(ax, title)
ax.set_xlabel(var_label)
# fixing the x tick labels are all squashed together
plt.gcf().autofmt_xdate()
plot.save_figure(fig_filename)
def plot_scatter(data,
x_column,
y_column,
title='',
x_var_name='',
x_var_unit='',
y_var_name='',
y_var_unit='',
circular=None,
fig_filename='',
mark_size=15):
plot.get_default_plot_style()
x_var_label = plot.get_var_label(x_var_name, x_var_unit)
y_var_label = plot.get_var_label(y_var_name, y_var_unit)
ax = sns.jointplot(x_column,
y_column,
data=data,
stat_func=None,
joint_kws={'s': mark_size},
marginal_kws={'hist_kws': {
'edgecolor': 'black'
}})
plt.subplots_adjust(top=0.9)
plt.gcf().suptitle(title)
if circular == 'x':
circular_labels, circular_ticks = plot.get_circular_ticks()
ax.ax_joint.set_xticks(circular_ticks)
ax.ax_joint.set_xticklabels(circular_labels, rotation=45)
plt.subplots_adjust(bottom=0.15)
elif circular == 'y':
circular_labels, circular_ticks = plot.get_circular_ticks()
ax.ax_joint.set_yticks(circular_ticks)
ax.ax_joint.set_yticklabels(circular_labels)
plt.subplots_adjust(left=0.15)
ax.set_axis_labels(x_var_label, y_var_label)
plot.save_figure(fig_filename)
def test_river_discharge_ecdf():
name = 'river_flow.txt'
data_path = os.path.join(tests.full_data_path, 'saih', name)
# Read Saih
data = saih.read_file(data_path)
# Empirical cdf
cumulative = True
data = empirical_distributions.kde_sm(data, cumulative=cumulative)
# Plot
plt.figure()
plt.show()
ax = plt.axes()
ax.plot(data.index, data)
plot.plot_title(ax, 'Cummulative density function')
ax.set_xlabel('$Q (m^3 \, s^{-1})$')
ax.set_ylabel('CDF')
plot.save_figure('CDF_river_discharge')
def plot_kde(data_empirical,
data_kernel,
cumulative,
title='',
var_name='',
var_unit='',
fig_filename='',
circular=False,
label_empirical='',
label_kernel=''):
plot.get_default_plot_style()
values_empirical = plot.get_values(data_empirical, '')
var_label = plot.get_var_label(var_name, var_unit)
ax = plt.axes()
ax.plot(values_empirical.index, values_empirical, label=label_empirical)
if cumulative:
values_kernel = plot.get_values(data_kernel, '')
ax.plot(values_kernel.index, values_kernel, label=label_kernel)
ax.legend(loc='upper right', facecolor='white')
plot.plot_title(ax, title)
if circular:
circular_labels, circular_ticks = plot.get_circular_ticks()
plt.yticks(circular_ticks, circular_labels)
plot.plot_title(ax, title)
ax.set_xlabel(var_label)
if cumulative:
ax.set_ylabel('CDF')
else:
ax.set_ylabel('PDF')
# fixing the x tick labels are all squashed together
plt.gcf().autofmt_xdate()
plot.save_figure(fig_filename)
def plot_series(data,
title='',
var_name='',
var_unit='',
fig_filename='',
data_column='',
circular=False,
show_trends=False,
label='observations'):
plot.get_default_plot_style()
values = plot.get_values(data, data_column)
var_label = plot.get_var_label(var_name, var_unit)
ax = plt.axes()
if show_trends is False:
ax.plot(values.index, values, linewidth=0.5)
else:
# TODO Comprobar que funciona correctamente con más ejemplos
# UnivariateSpline needs conversion from DateTimeIndex to float
splined_index = values.index.values.astype('d')
splined_values = UnivariateSpline(splined_index, values)
ax.plot(values.index, values, ':', alpha=0.3, label=label)
ax.plot(values.index, splined_values(splined_index))
ax.legend(loc='upper right', facecolor='white')
plot.plot_title(ax, title)
if circular:
circular_labels, circular_ticks = plot.get_circular_ticks()
plt.yticks(circular_ticks, circular_labels)
ax.set_ylabel(var_label)
# fixing the x tick labels are all squashed together
plt.gcf().autofmt_xdate()
plot.save_figure(fig_filename)
def plot_anual_variability(data,
daily_agg='mean',
plot_agg=np.mean,
ci='sd',
color='darkblue',
fig_filename=''):
plot.get_default_plot_style()
# Aggregate by day
data_aggregated = getattr(data.resample('D'), daily_agg)
# Split years
timeseries_data = time_series.generate_year_stack(data_aggregated())
# Fill gaps (one week maximum)
data_filled = timeseries_data.fillna(method='ffill', axis=1, limit=7)
# Remove incomplete years
data_clean = data_filled.dropna()
# Convert to numpy array
data_values = data_clean.values
sns.tsplot(data=data_values, estimator=plot_agg, ci=ci, color=color)
plot.save_figure(fig_filename)
def plot_ecdf_sm_dots(data,
title='',
var_name='',
var_unit='',
fig_filename='',
data_column='',
label='Observations'):
plot.get_default_plot_style()
values = plot.get_values(data, data_column)
var_label = plot.get_var_label(var_name, var_unit)
ax = plt.axes()
ax.plot(values.index, values, '.', label=label)
ax.legend(loc='upper right', facecolor='white')
plot.plot_title(ax, title)
ax.set_xlabel(var_label)
# fixing the x tick labels are all squashed together
plt.gcf().autofmt_xdate()
plt.show()
plot.save_figure(fig_filename)
def plot_empirical_fit(dist,
fitting_function,
params,
x_step=200,
cumulative=False,
fig_filename=''):
# width is in axis units
width, x_min, x_max = plot.calculation_width(dist)
plt.bar(dist.index, dist, width, edgecolor='black', linewidth=0.5)
x = np.linspace(x_min, x_max, x_step)
if cumulative:
cdf = getattr(fitting_function, 'cdf')
p = cdf(x, *params)
else:
pdf = getattr(fitting_function, 'pdf')
p = pdf(x, *params)
plt.plot(x, p, color='orange')
plot.save_figure(fig_filename)
