From c8e35ad8bef6686f505ce6675f82d4751dc5e34b Mon Sep 17 00:00:00 2001
From: "blaise.calmel" <blaise.calmel@gmail.com>
Date: Fri, 24 Nov 2023 19:57:32 +0100
Subject: [PATCH] [TEST] Test other functions for temporal interpolation
---
UI/main.py | 136 ++++++++++++++++++++++++++++++++++++++++-------------
1 file changed, 104 insertions(+), 32 deletions(-)
diff --git a/UI/main.py b/UI/main.py
index 0583a6e..1ea1900 100644
--- a/UI/main.py
+++ b/UI/main.py
@@ -13,7 +13,7 @@ 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/>.
+along with this program. If not, see <https://www.gnu.org/licenses/>.
"""
import os
@@ -26,7 +26,7 @@ import pickle
import pandas as pd
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
-from matplotlib.backends import backend_pdf, backend_svg, backend_ps # avoid bug after compilation
+# from matplotlib.backends import backend_pdf, backend_svg, backend_ps # avoid bug after compilation
import numpy as np
from datetime import datetime
@@ -1222,7 +1222,7 @@ class ApplicationWindow(QMainWindow, Ui_MainWindow):
self.sorted_name = list(self.mean_meas.index)
self.columns_checked = columns_checked_new
self.sticky_settings.set('columns_checked', self.columns_checked)
- self.run_interface()
+ self.table_meas()
def run_interface(self):
""" Plot graphs and tables
@@ -1240,43 +1240,42 @@ class ApplicationWindow(QMainWindow, Ui_MainWindow):
self.freeze = False
print('complete')
+ """essaie avec une fonction souple en DQ(t) (genre un polynôme ou une spline ou un fit non
+ paramétrique genre LOWESS?), sans x au début, puis on verra pour ajouter une propagation selon x"""
+
+ # Define values
+ import numpy as np
df = self.transects_df.loc[:, ['tr_q_total', 'meas_label_user', 'start_time', 'end_time']]
df['mid_time'] = (df['start_time'] + df['end_time']) / 2
df['mid_time'] = df['mid_time'] - np.nanmin(df['mid_time'])
df['meas_label_user'] = pd.to_numeric(df['meas_label_user'])
mean_q = np.nanmean(df['tr_q_total'])
df['delta_q'] = mean_q - df['tr_q_total']
- from sklearn import linear_model
- x = df[['meas_label_user', 'mid_time']]
- y = df['delta_q']
- regr = linear_model.LinearRegression()
- regr.fit(x, y)
- print('Intercept: \n', regr.intercept_)
- print('Coefficients: \n', regr.coef_)
+ X = df[['meas_label_user', 'mid_time']].values.reshape(-1, 2)
+ DQ = df['delta_q']
+ x = X[:, 0]
+ t = X[:, 1]
+ dq = DQ
+
+ # x = X[:, 0]
+ # y = X[:, 1]
+ # z = Y
- import statsmodels.api as sm
- x = sm.add_constant(x) # adding a constant
- model = sm.OLS(y, x).fit()
- predictions = model.predict(x)
- print_model = model.summary()
- print(print_model)
+ ############################## 3D LINEAR REGRESSION ##############################
import matplotlib.pyplot as plt
from sklearn import linear_model
from mpl_toolkits.mplot3d import Axes3D
- X = df[['meas_label_user', 'mid_time']].values.reshape(-1, 2)
- Y = df['delta_q']
- x = X[:, 0]
- y = X[:, 1]
- z = Y
+
+
x_pred = np.linspace(min(x), max(x), 30) # range of porosity values
- y_pred = np.linspace(min(y), max(y), 30) # range of brittleness values
- xx_pred, yy_pred = np.meshgrid(x_pred, y_pred)
- model_viz = np.array([xx_pred.flatten(), yy_pred.flatten()]).T
+ t_pred = np.linspace(min(t), max(t), 30) # range of brittleness values
+ xx_pred, tt_pred = np.meshgrid(x_pred, t_pred)
+ model_viz = np.array([xx_pred.flatten(), tt_pred.flatten()]).T
ols = linear_model.LinearRegression()
- model = ols.fit(X, Y)
+ model = ols.fit(X, DQ)
predicted = model.predict(model_viz)
- r2 = model.score(X, Y)
+ r2 = model.score(X, DQ)
plt.style.use('default')
fig = plt.figure(figsize=(12, 4))
@@ -1285,8 +1284,8 @@ class ApplicationWindow(QMainWindow, Ui_MainWindow):
ax3 = fig.add_subplot(133, projection='3d')
axes = [ax1, ax2, ax3]
for ax in axes:
- ax.plot(x, y, z, color='k', zorder=15, linestyle='none', marker='o', alpha=0.1)
- ax.scatter(xx_pred.flatten(), yy_pred.flatten(), predicted, facecolor=(0, 0, 0, 0), s=20,
+ ax.plot(x, t, dq, color='k', zorder=15, linestyle='none', marker='o', alpha=0.1)
+ ax.scatter(xx_pred.flatten(), tt_pred.flatten(), predicted, facecolor=(0, 0, 0, 0), s=20,
edgecolor='#70b3f0')
ax.set_xlabel('Distance (m)', fontsize=12)
ax.set_ylabel('Time (s)', fontsize=12)
@@ -1298,8 +1297,81 @@ class ApplicationWindow(QMainWindow, Ui_MainWindow):
ax3.view_init(elev=60, azim=165)
fig.suptitle('$R^2 = %.2f$' % r2, fontsize=20)
fig.tight_layout()
- fig.savefig(r"D:\0_INRAE\regression.png", dpi=300, bbox_inches='tight')
+ fig.savefig(r"D:\0_INRAE\linear_regression.png", dpi=300, bbox_inches='tight')
+
+ ############################## CURVE FIT INTERPOLATION ##############################
+ from scipy.optimize.minpack import curve_fit
+ fit_funcs = {"linear": lambda x, a: a * x ** self.exponent, "power": lambda x, a, b: a * x ** b, }
+ fit_func = 'power'
+
+ popt, pcov = curve_fit(fit_funcs[fit_func], t, dq)
+ ############################## POLY FIT INTERPOLATION ##############################
+ eq = np.polyfit(t, dq, 3)
+ f = np.poly1d(eq)
+ t_new = np.linspace(min(t), max(t), 50)
+ q_new = f(t_new)
+
+ fig = plt.figure(figsize=(12, 4))
+ ax = fig.add_subplot()
+ ax.scatter(t, dq, facecolor=(0, 0, 0, 0), s=20, edgecolor='#70b3f0')
+ ax.plot(t_new, q_new, color='k', zorder=15, alpha=0.9)
+ ax.set_xlabel('Time (s)', fontsize=12)
+ ax.set_ylabel('Delta Q (m3/s)', fontsize=12)
+ fig.savefig(r"D:\0_INRAE\polyfit_regression.png", dpi=300, bbox_inches='tight')
+
+ ############################## SLINEAR / QUADRATIC / CUBIC FUNCTIONS ##############################
+
+ import numpy as np
+ from scipy.interpolate import interp1d
+ import matplotlib.pyplot as plt
+ # Interpolation for different methods:
+ interpolations_methods = ['slinear', 'quadratic', 'cubic']
+ alpha = np.linspace(min(t), max(t), 1000)
+
+ interpolated_points = {}
+ for method in interpolations_methods:
+ interpolator = interp1d(t, dq, kind=method, axis=0)
+ interpolated_points[method] = interpolator(alpha)
+
+ fig = plt.figure(figsize=(12, 4))
+ ax = fig.add_subplot()
+ ax.scatter(t, dq, facecolor=(0, 0, 0, 0), s=20, edgecolor='#70b3f0', label='original points')
+ for method_name, curve in interpolated_points.items():
+ ax.plot(alpha, curve, '-', label=method_name)
+ # ax.plot(t_new, q_new, color='k', zorder=15, alpha=0.9)
+ ax.set_xlabel('Time (s)', fontsize=12)
+ ax.set_ylabel('Delta Q (m3/s)', fontsize=12)
+ ax.legend()
+ fig.savefig(r"D:\0_INRAE\slinear_quadratic_cubic__regression.png", dpi=300, bbox_inches='tight')
+
+ ############################## LOWESS FUNCTION ##############################
+
+ import matplotlib.pyplot as plt
+ from scipy.interpolate import interp1d
+ import statsmodels.api as sm
+ # lowess will return our "smoothed" data with a y value for at every x-value
+ lowess = sm.nonparametric.lowess(dq, t, frac=.3)
+ # unpack the lowess smoothed points to their values
+ lowess_t = list(zip(*lowess))[0]
+ lowess_dq = list(zip(*lowess))[1]
+ # run scipy's interpolation
+ f = interp1d(lowess_t, lowess_dq, bounds_error=False)
+ t_new = np.linspace(min(t), max(t), 1000)
+ # this this generate y values for our xvalues by our interpolator
+ # it will MISS values outsite of the x window
+ # There might be a better approach, but you can run a for loop
+ # and if the value is out of the range, use f(min(lowess_x)) or f(max(lowess_x))
+ dq_new = f(t_new)
+ fig = plt.figure(figsize=(12, 4))
+ ax = fig.add_subplot()
+ ax.scatter(t, dq, facecolor=(0, 0, 0, 0), s=20, edgecolor='#70b3f0', label='original points')
+ ax.plot(lowess_t, lowess_dq, '*', color='r', zorder=10, alpha=0.9, label='lowess points')
+ ax.plot(t_new, dq_new, color='k', zorder=15, alpha=0.9, label='lowess interpolation')
+ ax.set_xlabel('Time (s)', fontsize=12)
+ ax.set_ylabel('Delta Q (m3/s)', fontsize=12)
+ ax.legend()
+ fig.savefig(r"D:\0_INRAE\lowess_regression.png", dpi=300, bbox_inches='tight')
def load_tables(self):
""" Draw tables.
@@ -1882,9 +1954,9 @@ class ApplicationWindow(QMainWindow, Ui_MainWindow):
tbl.setHorizontalHeaderLabels(header_title)
header = tbl.horizontalHeader()
- for i in range(ncols-1):
- header.setSectionResizeMode(i, QtWidgets.QHeaderView.ResizeToContents)
- header.setSectionResizeMode(ncols-1, QtWidgets.QHeaderView.Stretch)
+ header.setSectionResizeMode(0, QtWidgets.QHeaderView.Stretch)
+ for i in range(1, ncols):
+ header.setSectionResizeMode(i, QtWidgets.QHeaderView.Interactive)
tbl.setVerticalHeaderLabels(self.mean_meas.index)
tbl.setSelectionBehavior(QtWidgets.QTableView.SelectRows)
--
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