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Calmel Blaise authored13229d7f
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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/>.
"""
import numpy as np
from PyQt5 import QtCore
from datetime import datetime
import matplotlib.dates as mdates
class FigTimeDischarge(object):
"""Class to plot chronological discharge graph.
Attributes
----------
canvas: MplCanvas
Object of MplCanvas a FigureCanvas
fig: Object
Figure object of the canvas
units: dict
Dictionary of units from units_conversion
_translate: QCoreApplication.translate object
Save words which need to be translated
hover_connection: int
Index to data cursor connection
annot: Annotation
Annotation object for data cursor
"""
def __init__(self, canvas, units):
"""Initialize object using the specified canvas.
Parameters
----------
canvas: MplCanvas
Object of MplCanvas
units: dict
Dictionary of units from units_conversion
"""
# Initialize attributes
self.canvas = canvas
self.fig = canvas.fig
self.units = units
self.hover_connection = None
self.annot = None
self._translate = QtCore.QCoreApplication.translate
def create(self, mean_selected_meas, selected_meas=None):
"""Create the axes and lines for the figure.
Parameters
----------
mean_selected_meas: pandas DataFrame
Measurement results dataframe
selected_meas: str
Name of the selected measurement
"""
# 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.subplots_adjust(left=0.03, bottom=0.25, right=0.99, top=0.99, wspace=0.1, hspace=0)
df = mean_selected_meas
df['color'] = 'k'
# Change color for selected
if selected_meas and selected_meas in df.meas_name.values:
df.loc[df['meas_name'] == selected_meas, 'color'] = 'darkorange'
# Plot horizontal line if there is less than 3 days
if df[['start_time', 'end_time']].max().max() - df[['start_time', 'end_time']].min().min() < 1210000:
is_time = ~np.logical_or(np.isnan(df.start_time), np.isnan(df.end_time))
df_time = df.loc[is_time, ['meas_name', 'tr_q_total', 'start_time', 'end_time', 'color']]
start_datetime = [datetime.utcfromtimestamp(i) for i in df_time.start_time]
end_datetime = [datetime.utcfromtimestamp(i) for i in df_time.end_time]
self.fig.ax.hlines(df_time.tr_q_total * self.units['Q'], start_datetime, end_datetime,
color=df_time.color, linewidth=2, zorder=0)
# Highlight selected measurement in orange
if selected_meas:
df_selected = df.loc[df['meas_name'] == selected_meas]
start_selected = [datetime.utcfromtimestamp(i) for i in df_selected.start_time if not np.isnan(i)]
end_selected = [datetime.utcfromtimestamp(i) for i in df_selected.end_time if not np.isnan(i)]
if len(start_selected) > 0 and len(start_selected) == len(end_selected):
self.fig.ax.hlines(df_selected.tr_q_total * self.units['Q'], start_selected, end_selected,
color=df_selected.color, linewidth=3, zorder=1)
# Plot data with only one date (start or end time) as scatter
df_missing_time = df.loc[~is_time, ['meas_name', 'tr_q_total', 'start_time', 'end_time', 'color']]
any_time = np.logical_or(~np.isnan(df_missing_time.start_time), ~np.isnan(df_missing_time.end_time))
df_scatter = df_missing_time[any_time]
df_scatter['time'] = df_scatter['start_time'].fillna(0) + df_scatter['end_time'].fillna(0)
scatter_datetime = [datetime.utcfromtimestamp(i) for i in df_scatter.time]
self.fig.ax.scatter(scatter_datetime, df_scatter['tr_q_total'] * self.units['Q'],
color=df_scatter.color, zorder=0)
self.fig.ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %H:%M'))
else:
df = df.groupby('meas_name').agg({'start_time': 'min', 'end_time': 'max', 'meas_mean_q': 'mean',
'color': 'first'})
mid_time = df[['start_time', 'end_time']].mean(axis=1)
is_time = ~np.isnan(mid_time)
mid_datetime = [datetime.utcfromtimestamp(i) for i in mid_time[is_time]]
self.fig.ax.scatter(mid_datetime, df.loc[is_time, 'meas_mean_q'] * self.units['Q'],
color=df.loc[is_time, 'color'], zorder=0)
self.fig.ax.xaxis.set_major_formatter(mdates.DateFormatter('%d %b %y'))
self.fig.ax.tick_params(which="major", axis="x", pad=14, size=2)
self.fig.autofmt_xdate()
self.fig.ax.set_xlabel(self._translate('Main', 'Time'))
self.fig.ax.set_ylabel(self._translate('Main', 'Discharge') + ' (' + self.units['label_Q'] + ')')
self.fig.ax.xaxis.label.set_fontsize(12)
self.fig.ax.yaxis.label.set_fontsize(12)
self.fig.ax.tick_params(axis='both', direction='in', bottom=True, top=True, left=True, right=True)
self.fig.ax.grid()
def hover(self, event):
"""Determines if the user has selected a location with temperature data and makes
annotation visible and calls method to update the text of the annotation. If the
location is not valid the existing annotation is hidden.
Parameters
----------
event: MouseEvent
Triggered when mouse button is pressed.
"""
# Set annotation to visible
vis = self.annot.get_visible()
# Determine if mouse location references a data point in the plot and update the annotation.
if event.inaxes == self.fig.ax and event.button != 3:
cont = False
ind = None
plotted_line = None
# Find the transect(line) that contains the mouse click
for plotted_line in self.fig.ax.lines:
cont, ind = plotted_line.contains(event)
if cont:
break
if cont:
self.update_annot(ind, plotted_line)
self.annot.set_visible(True)
self.canvas.draw_idle()
else:
# If the cursor location is not associated with the plotted data hide the annotation.
if vis:
self.annot.set_visible(False)
self.canvas.draw_idle()
def update_annot(self, ind, plt_ref):
"""Updates the location and text and makes visible the previously initialized and hidden annotation.
Parameters
----------
ind: dict
Contains data selected.
plt_ref: Line2D
Reference containing plotted data
vector_ref: Quiver
Refernece containing plotted data
ref_label: str
Label used to ID data type in annotation
"""
pos = plt_ref._xy[ind["ind"][0]]
# Shift annotation box left or right depending on which half of the axis the pos x is located and the
# direction of x increasing.
if plt_ref.axes.viewLim.intervalx[0] < plt_ref.axes.viewLim.intervalx[1]:
if pos[0] < (plt_ref.axes.viewLim.intervalx[0] + plt_ref.axes.viewLim.intervalx[1]) / 2:
self.annot._x = -20
else:
self.annot._x = -80
else:
if pos[0] < (plt_ref.axes.viewLim.intervalx[0] + plt_ref.axes.viewLim.intervalx[1]) / 2:
self.annot._x = -80
else:
self.annot._x = -20
# Shift annotation box up or down depending on which half of the axis the pos y is located and the
# direction of y increasing.
if plt_ref.axes.viewLim.intervaly[0] < plt_ref.axes.viewLim.intervaly[1]:
if pos[1] > (plt_ref.axes.viewLim.intervaly[0] + plt_ref.axes.viewLim.intervaly[1]) / 2:
self.annot._y = -40
else:
self.annot._y = 20
else:
if pos[1] > (plt_ref.axes.viewLim.intervaly[0] + plt_ref.axes.viewLim.intervaly[1]) / 2:
self.annot._y = 20
else:
self.annot._y = -40
self.annot.xy = pos
text = 'x: {:.2f}, y: {:.2f}'.format(pos[0], pos[1])
self.annot.set_text(text)
def set_hover_connection(self, setting):
"""Turns the connection to the mouse event on or off.
Parameters
----------
setting: bool
Boolean to specify whether the connection for the mouse event is active or not.
"""
if setting and self.hover_connection is None:
self.hover_connection = self.canvas.mpl_connect('button_press_event', self.hover)
elif not setting:
self.canvas.mpl_disconnect(self.hover_connection)
self.hover_connection = None
self.annot.set_visible(False)
self.canvas.draw_idle()