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acoused
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Inversion: Refactoring 'save_result_in_excel_file' method.

dev-brahim
Pierre-Antoine 2025-03-11 14:19:18 +01:00
parent 12fea4e182
commit ba0557294e
1 changed files with 68 additions and 91 deletions
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145
View/acoustic_inversion_tab.py
View File

@ -20,16 +20,11 @@
# -*- coding: utf-8 -*-
import pandas as pd
from PyQt5.QtWidgets import (QWidget, QVBoxLayout, QHBoxLayout, QGroupBox, QComboBox,
QLabel, QPushButton, QSpacerItem,
QSlider, QLineEdit, QMessageBox, QFileDialog)
from PyQt5.QtCore import QCoreApplication, Qt
from PyQt5.QtGui import QIcon, QPixmap
import os
import numpy as np
import pandas as pd
from copy import deepcopy
import matplotlib.pyplot as plt
@ -37,9 +32,14 @@ from matplotlib.colors import LogNorm
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolBar
from os import chdir
from PyQt5.QtWidgets import (
QWidget, QVBoxLayout, QHBoxLayout, QGroupBox, QComboBox,
QLabel, QPushButton, QSpacerItem, QSlider, QLineEdit,
QMessageBox, QFileDialog
)
from copy import deepcopy
from PyQt5.QtCore import QCoreApplication, Qt
from PyQt5.QtGui import QIcon, QPixmap
from View.checkable_combobox import CheckableComboBox
@ -1671,96 +1671,73 @@ class AcousticInversionTab(QWidget):
self.figure_measured_vs_inverted_sand.canvas.draw_idle()
def save_result_in_excel_file(self):
if self.combobox_acoustic_data_choice.count() > 0:
name = QFileDialog.getSaveFileName(
caption="Save As - Inversion results", directory="", filter="Excel Files (*.xlsx)",
options=QFileDialog.DontUseNativeDialog)
caption="Save As - Inversion results",
directory="",
filter="Excel Files (*.xlsx)",
options=QFileDialog.DontUseNativeDialog
)
if name[0]:
dirname = "/".join(name[0].split("/")[:-1]) + "/"
filename = name[0].split("/")[-1]
chdir(dirname)
dirname = os.path.dirname(name[0])
filename = os.path.basename(name[0])
os.chdir(dirname)
results = []
for k in range(self.combobox_acoustic_data_choice.count()):
if stg.time_cross_section[k].shape != (0,):
time_data = stg.time_cross_section
else:
time_data = stg.time
if stg.depth_cross_section[k].shape != (0,):
t = np.repeat(stg.time_cross_section[k][stg.frequency_for_inversion[1]],
stg.depth_cross_section[k].shape[1])
r = np.zeros((stg.depth_cross_section[k].shape[1] *stg.time_cross_section[k].shape[1],1))
for i in range(stg.time_cross_section[k].shape[1]):
for j in range(stg.depth_cross_section[k].shape[1]):
r[i * stg.depth_cross_section[k].shape[1] + j] = (
stg.depth_cross_section[k][int(stg.frequency_for_inversion[1]), j])
if stg.SSC_fine[k].shape == (0,):
stg.SSC_fine[k] = np.zeros(r.shape[0])
if stg.SSC_sand[k].shape == (0,):
stg.SSC_sand[k] = np.zeros(r.shape[0])
depth_data = stg.depth_cross_section
else:
depth_data = stg.depth
t = np.repeat(stg.time_cross_section[k][stg.frequency_for_inversion[1]], stg.depth[k].shape[1])
r = np.zeros((stg.depth[k].shape[1] * stg.time_cross_section[k].shape[1], 1))
for i in range(stg.time_cross_section[k].shape[1]):
for j in range(stg.depth[k].shape[1]):
r[i * stg.depth[k].shape[1] + j] = (
stg.depth[k][int(stg.frequency_for_inversion[1]), j])
t = np.repeat(
time_data[k][stg.frequency_for_inversion[1]],
depth_data[k].shape[1]
)
r = np.zeros((
depth_data[k].shape[1] * time_data[k].shape[1], 1
))
for i in range(time_data[k].shape[1]):
for j in range(depth_data[k].shape[1]):
r_id = i * depth_data[k].shape[1] + j
r[r_id] = (
depth_data[k][
int(stg.frequency_for_inversion[1]), j
]
)
if stg.SSC_fine[k].shape == (0,):
stg.SSC_fine[k] = np.zeros(r.shape[0])
if stg.SSC_sand[k].shape == (0,):
stg.SSC_sand[k] = np.zeros(r.shape[0])
else:
results.append(
pd.DataFrame(
{
'Time (sec)': t, 'Depth (m)': r,
'SSC_fine (g/L)': stg.SSC_fine[k].reshape(t.shape[0]),
'SSC_sand (g/L)': stg.SSC_sand[k].reshape(t.shape[0]),
}
)
)
if stg.depth_cross_section[k].shape != (0,):
t = np.repeat(stg.time[k][stg.frequency_for_inversion[1]], stg.depth_cross_section[k].shape[1])
r = np.zeros((stg.depth_cross_section[k].shape[1] * stg.time[k].shape[1], 1))
for i in range(stg.time[k].shape[1]):
for j in range(stg.depth_cross_section[k].shape[1]):
r[i * stg.depth_cross_section[k].shape[1] + j] = (
stg.depth_cross_section[k][int(stg.frequency_for_inversion[1]), j])
if stg.SSC_fine[k].shape == (0,):
stg.SSC_fine[k] = np.zeros(r.shape[0])
if stg.SSC_sand[k].shape == (0,):
stg.SSC_sand[k] = np.zeros(r.shape[0])
else:
t = np.repeat(stg.time[k][stg.frequency_for_inversion[1]], stg.depth[k].shape[1])
r = np.zeros(stg.depth[k].shape[1] * stg.time[k].shape[1])
for i in range(stg.time[k].shape[1]):
for j in range(stg.depth[k].shape[1]):
r[i * stg.depth[k].shape[1] + j] = (
stg.depth[k][int(stg.frequency_for_inversion[1]), j])
if stg.SSC_fine[k].shape == (0,):
stg.SSC_fine[k] = np.zeros(r.shape[0])
if stg.SSC_sand[k].shape == (0,):
stg.SSC_sand[k] = np.zeros(r.shape[0])
exec("result_" + str(k) + "= pd.DataFrame({'Time (sec)': t," +
"'Depth (m)': r," +
"'SSC_fine (g/L)': stg.SSC_fine[" + str(k) + "].reshape(t.shape[0])," +
"'SSC_sand (g/L)': stg.SSC_sand[" + str(k) + "].reshape(t.shape[0])})")
with pd.ExcelWriter(dirname + filename + '.xlsx') as writer:
with pd.ExcelWriter(
os.path.join(dirname, filename + '.xlsx')
) as writer:
for k in range(self.combobox_acoustic_data_choice.count()):
eval("result_" + str(k) + ".to_excel(writer, index=False, " +
"engine='xlsxwriter', na_rep='NA', " +
"sheet_name=stg.data_preprocessed[" + str(k) + "])")
results[k].to_excel(
writer, index=False,
engine='xlsxwriter', na_rep='NA',
sheet_name=stg.data_preprocessed[k],
)