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Delaigue Olivier authoredddf30bac
Forked from
HYCAR-Hydro / airGR
Source project has a limited visibility.
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"""
QRame
Copyright (C) 2023 INRAE
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as published
by the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
from PyQt5 import QtCore
import numpy as np
import pandas as pd
class FigUncertaintySources(object):
"""Class to plot uncertainty sources distribution graph.
Attributes
----------
canvas: MplCanvas
Object of MplCanvas a FigureCanvas
fig: Object
Figure object of the canvas
_translate: QCoreApplication.translate object
Save words which need to be translated
"""
def __init__(self, canvas):
"""Initialize object using the specified canvas.
Parameters
----------
canvas: MplCanvas
Object of MplCanvas
"""
# Initialize attributes
self.canvas = canvas
self.fig = canvas.fig
self._translate = QtCore.QCoreApplication.translate
def create(self, mean_selected_meas, no_moving_bed=False, column_grouped=None,
column_sorted=None, ascending=True):
"""Create the axes and lines for the figure.
Parameters
----------
mean_selected_meas: pandas DataFrame
Measurement results dataframe
no_moving_bed: bool
Specify if there is no moving bed observed
column_grouped: str
Columns name that could be used for grouped analysis
column_sorted: str
Columns name used to sort dataframe
ascending: bool
Specify if current data are sorted ascending or descending
"""
# Clear the plot
self.fig.clear()
# Configure axis
self.fig.ax = self.fig.add_subplot(1, 1, 1)
# Set margins and padding for figure
self.fig.ax.xaxis.label.set_fontsize(12)
self.fig.ax.yaxis.label.set_fontsize(12)
list_basic_color = ['#696969', '#808080', '#A9A9A9', '#00BFFF', '#0000FF', '#FF6666', '#FF0000', '#CC0000',
'#FF00FF', '#EE82EE', '#20B2AA', '#008B8B', '#00FFFF']
# Uncertainty decomposition
labels = [self._translate("Main", 'Systematic'), self._translate("Main", 'Compass'),
self._translate("Main", 'Moving-bed'), self._translate("Main", 'Measured'),
self._translate("Main", 'Nb of ens.'), self._translate("Main", 'Invalid boat'),
self._translate("Main", 'Invalid depth'), self._translate("Main", 'Invalid water'),
self._translate("Main", 'Top'), self._translate("Main", 'Bottom'),
self._translate("Main", 'Left'), self._translate("Main", 'Right'),
self._translate("Main", 'Coeff. of var.')]
col_name = ['tr_oursin_u_syst', 'tr_oursin_u_compass', 'tr_oursin_u_movbed', 'tr_oursin_u_meas',
'tr_oursin_u_ens', 'tr_oursin_u_boat', 'tr_oursin_u_depth', 'tr_oursin_u_water', 'tr_oursin_u_top',
'tr_oursin_u_bot', 'tr_oursin_u_left', 'tr_oursin_u_right', 'tr_oursin_u_cov', 'meas_ntransects',
'meas_oursin_95']
col_name_2 = ['o_2_syst', 'o_2_compass', 'o_2_movbed', 'o_2_meas', 'o_2_ens', 'o_2_badb', 'o_2_badd',
'o_2_badw', 'o_2_top', 'o_2_bot', 'o_2_left', 'o_2_rght', 'o_2_cov']
if no_moving_bed:
mean_selected_meas['tr_oursin_u_movbed'] = 0.5
# Variance
for i in range(len(col_name_2)):
mean_selected_meas[col_name_2[i]] = mean_selected_meas[col_name[i]] ** 2
# Random uncertainty sources
mean_selected_meas['o_2_meas'] = mean_selected_meas.o_2_meas / mean_selected_meas.meas_ntransects
mean_selected_meas['o_2_cov'] = mean_selected_meas.o_2_cov / mean_selected_meas.meas_ntransects
if column_grouped is not None:
# Group data
mean_selected_meas[column_grouped] = mean_selected_meas[column_grouped].fillna(
self._translate("Main", 'Unknown'))
grouped_meas = mean_selected_meas.groupby([column_grouped]).median()
if column_sorted is not None:
if mean_selected_meas.dtypes[column_sorted] in (float, int):
grouped_meas = mean_selected_meas.groupby([column_grouped]).median()
else:
grouped_str = mean_selected_meas.groupby([column_grouped]).agg({
column_sorted: lambda x: x.value_counts().index[0]})
grouped_meas = pd.concat([grouped_meas, grouped_str], axis=1)
grouped_meas.index.names = ['index']
grouped_meas = grouped_meas.sort_values(column_sorted, ascending=ascending)
mean_selected_meas = grouped_meas
# Plot empirical uncertainty bar by group
mean_oursin = mean_selected_meas['meas_oursin_95']
mean_empirical = mean_selected_meas[['U_Q_n', 'U_Q_n_min', 'U_Q_n_max']]
p1 = self.fig.ax.bar(np.arange(len(mean_empirical)), mean_empirical['U_Q_n'],
# yerr=[mean_empirical['U_Q_n']-mean_empirical['U_Q_n_min'],
# mean_empirical['U_Q_n']+mean_empirical['U_Q_n_max']],
align='center', color='gray', edgecolor='black', ecolor='black',
linewidth=2.5, alpha=0.3, width=0.6, capsize=10)
else:
sorted_names = list(mean_selected_meas.index)
mean_oursin = mean_selected_meas.groupby('meas_name')['meas_oursin_95'].mean()
mean_oursin = mean_oursin.reindex(sorted_names)
# Plot uncertainty sources bar
mean_selected_meas['Sum_n'] = mean_selected_meas[col_name_2].sum(axis=1)
mean_plot = mean_selected_meas[col_name_2].div(mean_selected_meas['Sum_n'].values, axis=0).mul(
mean_oursin.values, axis=0)
my_xticks = [l for l in mean_plot.index]
if len(mean_plot) > 0:
mean_plot.plot(kind='bar', stacked=True, ax=self.fig.ax, legend=False, color=list_basic_color, zorder=1,
width=0.4)
# Legend
handles, _ = self.fig.ax.get_legend_handles_labels()
handles_top = list(reversed(handles))
labels_top = list(reversed(labels))
if column_grouped is not None:
handles_top.append(p1)
labels_top.append(self._translate("Main", 'Empirical uncertainty'))
self.fig.ax.legend(handles_top,
labels_top,
loc='upper left',
bbox_to_anchor=(1, 1),
labelspacing=0.25,
fontsize=14,
fancybox=True,
shadow=True)
self.fig.ax.set_ylabel(self._translate("Main", 'Uncertainty') + ' (%)', fontsize=11)
self.fig.ax.set_xlabel(None)
if sum(len(s) for s in my_xticks[1:]) < 80:
self.fig.ax.set_xticklabels(my_xticks, rotation=0, fontsize=10)
else:
self.fig.ax.set_xticklabels(my_xticks, rotation=80, fontsize=10, ha='right')
self.fig.ax.grid(linestyle='--')
# self.fig.tight_layout()
# self.canvas.draw()