调整缩放、多核运行、图标显示

1.py

@@ -0,0 +1,4 @@
+
+
+new_wavelengths = [np.mean(wavelengths[i:i+3]) for i in range(0, len(wavelengths), 3)]
+print(new_wavelengths)

src/core/glint_removal/Goodman.py

@@ -1,4 +1,5 @@
 import numpy as np
+import sys
 # import preprocessing
 
 try:
@@ -14,9 +15,12 @@ try:
 except ImportError:
     TQDM_AVAILABLE = False
     # 如果tqdm不可用，定义一个简单的包装器
-    def tqdm(iterable, desc=None, total=None):
+    def tqdm(iterable, desc=None, total=None, disable=None):
         return iterable
 
+# 检测是否在 PyInstaller 打包环境（无控制台）
+_is_frozen_gui = getattr(sys, "frozen", False) and (not hasattr(sys, 'stdout') or sys.stdout is None)
+
 class Goodman:
     def __init__(self, im_aligned, NIR_lower = 25, NIR_upper = 37, A = 0.000019, B = 0.1, 
                  use_gdal=True, chunk_size=None, water_mask=None, output_path=None):
@@ -170,7 +174,7 @@ class Goodman:
         water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None
         
         # 逐波段处理：每次只处理一个波段，处理完后立即添加到结果列表
-        for i in tqdm(range(self.n_bands), desc="处理波段 (numpy)", total=self.n_bands):
+        for i in tqdm(range(self.n_bands), desc="处理波段 (numpy)", total=self.n_bands, disable=_is_frozen_gui):
             # 获取当前波段（这是数组视图，不是复制）
             R = self.im_aligned[:,:,i]
             # 优化计算：减少中间数组创建
@@ -207,7 +211,7 @@ class Goodman:
         water_mask_bool = self.water_mask.astype(bool) if self.water_mask is not None else None
         
         # 逐波段处理：每次只读取和处理一个波段
-        for i in tqdm(range(self.n_bands), desc="处理波段 (GDAL)", total=self.n_bands):
+        for i in tqdm(range(self.n_bands), desc="处理波段 (GDAL)", total=self.n_bands, disable=_is_frozen_gui):
             # 读取当前波段（只加载一个波段到内存）
             current_band = self.dataset.GetRasterBand(i + 1)
             R = current_band.ReadAsArray().astype(np.float32)
@@ -235,7 +239,7 @@ class Goodman:
         mem_dataset = driver.Create('', self.width, self.height, self.n_bands, gdal.GDT_Float32)
         
         # 将numpy数组写入内存数据集（显示进度）
-        for i in tqdm(range(self.n_bands), desc="加载波段到内存", total=self.n_bands):
+        for i in tqdm(range(self.n_bands), desc="加载波段到内存", total=self.n_bands, disable=_is_frozen_gui):
             band = mem_dataset.GetRasterBand(i + 1)
             band.WriteArray(self.im_aligned[:,:,i])
             band.FlushCache()
@@ -316,7 +320,7 @@ class Goodman:
                 dataset.SetProjection(projection)
             
             # 直接逐波段写入（不先堆叠，节省内存）
-            for i in tqdm(range(n_bands), desc="保存波段", total=n_bands):
+            for i in tqdm(range(n_bands), desc="保存波段", total=n_bands, disable=_is_frozen_gui):
                 band = dataset.GetRasterBand(i + 1)
                 # 直接从列表中获取波段并写入，避免创建完整数组
                 band.WriteArray(corrected_bands[i])

src/core/modeling/modeling_batch.py

@@ -19,7 +19,7 @@ from sklearn.cross_decomposition import PLSRegression
 from sklearn.ensemble import GradientBoostingRegressor, AdaBoostRegressor, ExtraTreesRegressor
 from sklearn.tree import DecisionTreeRegressor
 from sklearn.neural_network import MLPRegressor
-
+from joblib import parallel_backend
 # 第三方模型导入
 # try:
 #     import lightgbm as lgb
@@ -648,6 +648,8 @@ class WaterQualityModelingBatch:
         )
 
         # 在训练集上训练模型
+        # with parallel_backend("threading", n_jobs=-1):
+        #     grid_search.fit(X_train, y_train)
         grid_search.fit(X_train, y_train)
         
         # 获取最佳模型

src/gui/water_quality_gui.py

@@ -15,7 +15,7 @@ from datetime import datetime
 from typing import Dict, Optional, List, Union
 import numpy as np
 import pandas as pd
-
+import multiprocessing
 from PyQt5.QtWidgets import (
     QApplication, QMainWindow, QWidget, QVBoxLayout, QHBoxLayout, 
     QPushButton, QLabel, QLineEdit, QComboBox, QCheckBox, QSpinBox, 
@@ -3462,6 +3462,10 @@ class ImageViewerWidget(QWidget):
         super().__init__(parent)
         self.current_image_path = None
         self.scale_factor = 1.0
+        self._update_timer = QTimer()  # 防抖定时器
+        self._update_timer.setSingleShot(True)
+        self._update_timer.timeout.connect(self._do_update_display)
+        self._pending_scale = None  # 待更新的缩放比例
         self.setup_ui()
     
     def setup_ui(self):
@@ -3564,28 +3568,62 @@ class ImageViewerWidget(QWidget):
         self.status_label.setText(f"{pixmap.width()}x{pixmap.height()} | {size_mb:.2f} MB | {Path(image_path).name} | 适应窗口")
     
     def update_image_display(self):
-        """更新图像显示"""
+        """更新图像显示 - 使用防抖避免频繁重绘卡顿"""
+        # 取消之前的待执行更新，重新计时
+        self._update_timer.stop()
+        self._pending_scale = self.scale_factor
+        self._update_timer.start(50)  # 50ms后执行实际更新
+    
+    def _do_update_display(self):
+        """实际执行图像更新"""
         if not hasattr(self, 'original_pixmap') or self.original_pixmap.isNull():
             return
         
+        if self._pending_scale is None:
+            return
+        
+        # 根据缩放比例选择变换模式：大幅度缩放用Fast模式提升性能
+        if self._pending_scale > 2.0 or self._pending_scale < 0.5:
+            transform = Qt.FastTransformation
+        else:
+            transform = Qt.SmoothTransformation
+        
         scaled_pixmap = self.original_pixmap.scaled(
-            int(self.original_pixmap.width() * self.scale_factor),
-            int(self.original_pixmap.height() * self.scale_factor),
+            int(self.original_pixmap.width() * self._pending_scale),
+            int(self.original_pixmap.height() * self._pending_scale),
             Qt.KeepAspectRatio,
-            Qt.SmoothTransformation
+            transform
         )
         self.image_label.setPixmap(scaled_pixmap)
+        self._pending_scale = None
+    
+    def wheelEvent(self, event):
+        """鼠标滚轮缩放 - 实时响应"""
+        delta = event.angleDelta().y()
+        
+        if delta > 0:
+            # 向上滚动 - 放大
+            if self.scale_factor < 5.0:
+                self.scale_factor = min(self.scale_factor * 1.1, 5.0)
+                self.update_image_display()
+        else:
+            # 向下滚动 - 缩小
+            if self.scale_factor > 0.1:
+                self.scale_factor = max(self.scale_factor / 1.1, 0.1)
+                self.update_image_display()
+        
+        event.accept()
     
     def zoom_in(self):
         """放大"""
         if self.scale_factor < 5.0:
-            self.scale_factor *= 1.25
+            self.scale_factor = min(self.scale_factor * 1.25, 5.0)
             self.update_image_display()
     
     def zoom_out(self):
         """缩小"""
         if self.scale_factor > 0.1:
-            self.scale_factor /= 1.25
+            self.scale_factor = max(self.scale_factor / 1.25, 0.1)
             self.update_image_display()
     
     def fit_to_window(self):
@@ -3599,14 +3637,20 @@ class ImageViewerWidget(QWidget):
         
         scale_w = view_size.width() / img_size.width()
         scale_h = view_size.height() / img_size.height()
-        self.scale_factor = min(scale_w, scale_h, 1.0)  # 不超过原始大小
+        
+        # 记录适应前的比例（用于后续恢复参考）
+        self._fit_scale = min(scale_w, scale_h)
+        self.scale_factor = self._fit_scale
         
         self.update_image_display()
+        self.status_label.setText(f"适应窗口 | 缩放: {self.scale_factor:.1%}")
     
     def original_size(self):
         """原始大小"""
         self.scale_factor = 1.0
+        self._fit_scale = None  # 清除适应记录
         self.update_image_display()
+        self.status_label.setText("原始大小 | 缩放: 100%")
     
     def save_image(self):
         """保存图像"""
@@ -5229,6 +5273,7 @@ class WaterQualityGUI(QMainWindow):
         
         self.init_ui()
         self.apply_stylesheet()
+        self._disable_wheel_for_all_spinboxes()
 
     def get_icon_path(self, icon_filename):
         """
@@ -5245,10 +5290,48 @@ class WaterQualityGUI(QMainWindow):
 
         return os.path.join(icon_dir, icon_filename)
 
+    def _disable_wheel_for_all_spinboxes(self):
+        """
+        遍历所有子控件，为 QSpinBox/QDoubleSpinBox/QComboBox 禁用滚轮事件
+        防止滚动页面时意外改变数值
+        """
+        from PyQt5.QtCore import Qt
+
+        # 找到所有数值输入控件
+        for spinbox in self.findChildren(QSpinBox):
+            spinbox.setFocusPolicy(Qt.StrongFocus)  # 只有聚焦时才响应滚轮
+            spinbox.wheelEvent = lambda event, sb=spinbox: None  # 完全禁用滚轮
+
+        for spinbox in self.findChildren(QDoubleSpinBox):
+            spinbox.setFocusPolicy(Qt.StrongFocus)
+            spinbox.wheelEvent = lambda event, sb=spinbox: None
+
+        for combobox in self.findChildren(QComboBox):
+            combobox.setFocusPolicy(Qt.StrongFocus)
+            combobox.wheelEvent = lambda event, cb=combobox: None
+        
     def init_ui(self):
         """初始化UI"""
         self.setWindowTitle("水质参数反演分析系统 v1.0")
-        self.setGeometry(100, 100, 1200, 800)
+
+        # 获取屏幕可用区域（排除任务栏）
+        screen_geometry = QApplication.primaryScreen().availableGeometry()
+        screen_width = screen_geometry.width()
+        screen_height = screen_geometry.height()
+
+        # 初始尺寸：宽度固定 800，高度占满屏幕
+        window_width = 1200
+        window_height = screen_height
+        # 仅设置初始大小，不锁定
+        self.resize(window_width, window_height)
+
+        # 计算水平居中、垂直贴顶的位置
+        x = (screen_width - window_width) // 2
+        y = 0
+        self.move(x, y)
+
+        # 可选：设置最小尺寸，防止用户缩得太小
+        self.setMinimumSize(600, 400)
         
         # 创建自定义标题栏（包含Logo和菜单栏）
         self.create_title_bar()
@@ -5297,8 +5380,12 @@ class WaterQualityGUI(QMainWindow):
         """)
         
         # 设置Logo图片路径 - 使用相对路径（打包兼容）
-        logo_path = r"E:\code\WQ\GUI_v1\fengzhuang-ui2V3\data\icons\logo.png"
-        logo_pixmap = QPixmap(str(logo_path))
+        from pathlib import Path
+        if hasattr(sys, '_MEIPASS'):
+            logo_path = os.path.join(sys._MEIPASS, 'data', 'icons', 'logo.png')
+        else:
+            logo_path = str(Path(__file__).parent.parent.parent / "data" / "icons" / "logo.png")
+        logo_pixmap = QPixmap(logo_path)
         
         if not logo_pixmap.isNull():
             # 按高度缩放图片，保持宽高比，让Logo更显眼
@@ -5406,17 +5493,23 @@ class WaterQualityGUI(QMainWindow):
         banner_layout = QHBoxLayout()
         banner_layout.setContentsMargins(0, 0, 0, 0)
         banner_layout.setSpacing(0)
+        # 不设置居中对齐，让横幅填满整个容器
 
-        # 创建横幅标签
+        # 创建横幅标签 - 完全跟随窗口等比缩放，填满整个区域
         self.banner_label = QLabel()
-        self.banner_label.setMinimumHeight(65)
-        self.banner_label.setMaximumHeight(110)
-        self.banner_label.setAlignment(Qt.AlignCenter)
+        # 最小高度保证：当窗口很小时至少显示 38px 高 (200px 宽 / 5.25)
+        self.banner_label.setMinimumHeight(int(200 / 5.25))   # ≈ 38px
+        # 使用 Expanding 策略让标签填满可用空间
         self.banner_label.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Fixed)
         self.banner_label.setScaledContents(False)
+        # 清除 QLabel 默认的 margin 和 padding，消除右侧空白
+        self.banner_label.setStyleSheet("margin: 0px; padding: 0px; border: none;")
 
         # 保存原始pixmap用于后续缩放
-        banner_path = r"E:\code\WQ\GUI_v1\fengzhuang-ui2\data\icons\Mega Water 1.0.png"
+        if hasattr(sys, '_MEIPASS'):
+            banner_path = os.path.join(sys._MEIPASS, 'data', 'icons', 'Mega Water 1.0.png')
+        else:
+            banner_path = str(Path(__file__).parent.parent.parent / "data" / "icons" / "Mega Water 1.0.png")
         self.banner_pixmap = QPixmap(banner_path)
 
         if not self.banner_pixmap.isNull():
@@ -5444,13 +5537,19 @@ class WaterQualityGUI(QMainWindow):
         banner_toolbar.setMovable(False)
         banner_toolbar.setFloatable(False)
         banner_toolbar.addWidget(banner_widget)
+        banner_toolbar.setContentsMargins(0, 0, 0, 0)  # 清除工具栏布局的边距
         banner_toolbar.setStyleSheet("""
             QToolBar {
                 background-color: white;
                 border: none;
                 border-bottom: 1px solid #ddd;
-                padding: 2px 0px;
+                padding: 0px;
                 margin: 0px;
+                spacing: 0px;
+            }
+            QToolBar QWidget {
+                margin: 0px;
+                padding: 0px;
             }
         """)
 
@@ -6203,27 +6302,32 @@ class WaterQualityGUI(QMainWindow):
         self.training_mode_action.setText("有训练数据模式" if checked else "无训练数据模式")
 
     def update_banner_image(self):
-        """更新横幅图片 - 等比自适应缩放"""
+        """更新横幅图片 - 完全跟随窗口等比缩放，填满可用宽度"""
         if not hasattr(self, 'banner_pixmap') or self.banner_pixmap.isNull():
             return
 
-        # 获取可用宽度（考虑工具栏边距）
-        available_width = max(200, self.width() - 60)   # 最小宽度保护
+        # 获取可用宽度（考虑工具栏边距），跟随窗口实时变化
+        available_width = max(200, self.width() - 60)
         
-        # 第一步：按宽度缩放，保持比例
-        scaled_pixmap = self.banner_pixmap.scaled(
-            available_width,
-            120,                    # 最大允许高度
-            Qt.KeepAspectRatio,     # 关键：等比缩放
-            Qt.SmoothTransformation # 平滑缩放
-        )
+        # 先根据可用宽度计算目标高度（严格 5.25:1）
+        target_height = int(available_width / 5.25)
         
-        # 如果高度仍然过大，则按高度限制缩放
-        if scaled_pixmap.height() > 110:
+        # 限制最小高度
+        if target_height < 38:
+            target_height = 38
+            available_width = int(38 * 5.25)
+
+        # 计算图片目标尺寸（保持 5.25:1 比例）
+        target_width = available_width
+        
+        # 设置固定尺寸，确保标签严格填满整个区域
+        self.banner_label.setFixedSize(target_width, target_height)
+
+        # 等比缩放到目标尺寸，填满整个区域（允许轻微裁剪）
         scaled_pixmap = self.banner_pixmap.scaled(
-                int(available_width * 0.9),
-                110,
-                Qt.KeepAspectRatio,
+            target_width,
+            target_height,
+            Qt.KeepAspectRatioByExpanding,  # 保持比例，填满区域，允许裁剪超出部分
             Qt.SmoothTransformation
         )
 
@@ -6232,15 +6336,8 @@ class WaterQualityGUI(QMainWindow):
     def resizeEvent(self, event):
         """窗口大小改变事件 - 实时更新横幅图片等比缩放"""
         super().resizeEvent(event)
-        # 使用定时器避免频繁调用
-        if hasattr(self, '_banner_timer'):
-            self._banner_timer.stop()
-        else:
-            self._banner_timer = QTimer()
-            self._banner_timer.setSingleShot(True)
-            self._banner_timer.timeout.connect(self.update_banner_image)
-        
-        self._banner_timer.start(50)  # 50ms后更新
+        # 直接调用，不使用定时器延迟（或缩短到 10ms）
+        self.update_banner_image()
 
     def update_ui_for_training_mode(self):
         """根据训练数据模式更新UI状态"""
@@ -6296,5 +6393,7 @@ def main():
 
 
 if __name__ == "__main__":
+    #冻结，只显示1个exe
+    # multiprocessing.freeze_support()
     main()
 

src/gui/work_dir/2_glint/severe_glint_area.hdr

@@ -1,15 +0,0 @@
-ENVI
-description = {
-work_dir\2_glint\severe_glint_area.dat}
-samples = 11363
-lines   = 10408
-bands   = 1
-header offset = 0
-file type = ENVI Standard
-data type = 4
-interleave = bsq
-byte order = 0
-map info = {UTM, 1, 1, 600742.055, 4613386.65, 0.2, 0.2, 51, North,WGS-84}
-coordinate system string = {PROJCS["WGS_1984_UTM_Zone_51N",GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",123.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]}
-band names = {
-Band 1}

src/gui/work_dir/6_75_custom_regression/1002.515991_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,1002.515991,0.11209397634185636,y = -0.005956 + 0.001186*x,134,10.960663313432837,3.9096921347220377,0.007041335820895523,0.0138473135041692
-logarithmic,Chlorophyll,1002.515991,0.09022914646608904,y = -0.019813 + 0.011526*ln(x),134,10.960663313432837,3.9096921347220377,0.007041335820895523,0.0138473135041692

src/gui/work_dir/6_75_custom_regression/1007.041016_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,1007.041016,0.13129585788396014,y = -0.007873 + 0.001537*x,134,10.960663313432837,3.9096921347220377,0.008974216417910446,0.016584272713125302
-logarithmic,Chlorophyll,1007.041016,0.10398887849221805,y = -0.025553 + 0.014819*ln(x),134,10.960663313432837,3.9096921347220377,0.008974216417910446,0.016584272713125302

src/gui/work_dir/6_75_custom_regression/1011.56897_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,1011.56897,0.11897246869922418,y = -0.007866 + 0.001621*x,134,10.960663313432837,3.9096921347220377,0.00990283582089552,0.01837518499092342
-logarithmic,Chlorophyll,1011.56897,0.09605697495450882,y = -0.026865 + 0.015780*ln(x),134,10.960663313432837,3.9096921347220377,0.00990283582089552,0.01837518499092342

src/gui/work_dir/6_75_custom_regression/374.285004_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,374.285004,0.0577461915301245,y = 0.009707 + 0.000311*x,134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534
-logarithmic,Chlorophyll,374.285004,0.052490162787109385,y = 0.005636 + 0.003209*ln(x),134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534
-exponential,Chlorophyll,374.285004,0.030557192829324564,y = 0.010822 * exp(0.013060*x),134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534
-power,Chlorophyll,374.285004,0.02576326804736484,y = 0.009209 * x^0.130700,134,10.960663313432837,3.9096921347220377,0.013112298507462688,0.005054260878733534

src/gui/work_dir/6_75_custom_regression/378.311005_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,378.311005,0.008061439581006025,y = 0.013092 + 0.001044*ln(x),134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235
-linear,Chlorophyll,378.311005,0.008052879252108514,y = 0.014468 + 0.000096*x,134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235
-power,Chlorophyll,378.311005,-0.016155019039159058,y = 0.015641 * x^-0.016124,134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235
-exponential,Chlorophyll,378.311005,-0.01708357282563666,y = 0.015362 * exp(-0.001784*x),134,10.960663313432837,3.9096921347220377,0.01552370895522388,0.00419444858565235

src/gui/work_dir/6_75_custom_regression/382.341003_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,382.341003,0.010983531384569756,y = 0.013856 + 0.000202*x,134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279
-logarithmic,Chlorophyll,382.341003,0.010636805221273526,y = 0.011048 + 0.002157*ln(x),134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279
-power,Chlorophyll,382.341003,-0.007234268459601845,y = 0.015174 * x^0.006143,134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279
-exponential,Chlorophyll,382.341003,-0.008026222697967267,y = 0.015380 * exp(0.000073*x),134,10.960663313432837,3.9096921347220377,0.016074067164179102,0.007548431031201279

src/gui/work_dir/6_75_custom_regression/386.373993_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,386.373993,0.004476522091747537,y = 0.013943 + 0.001356*ln(x),134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564
-linear,Chlorophyll,386.373993,0.003937809502393641,y = 0.015816 + 0.000117*x,134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564
-power,Chlorophyll,386.373993,-0.013451645087448227,y = 0.018099 * x^-0.040970,134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564
-exponential,Chlorophyll,386.373993,-0.01526209268366463,y = 0.017374 * exp(-0.004977*x),134,10.960663313432837,3.9096921347220377,0.017102343283582087,0.007312955327938564

src/gui/work_dir/6_75_custom_regression/390.410004_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,390.410004,0.0033869580773776553,y = 0.014722 + 0.001210*ln(x),134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778
-linear,Chlorophyll,390.410004,0.0026484411391527463,y = 0.016458 + 0.000099*x,134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778
-power,Chlorophyll,390.410004,-0.01625121404032659,y = 0.019411 * x^-0.060440,134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778
-exponential,Chlorophyll,390.410004,-0.018540174411018073,y = 0.018243 * exp(-0.007186*x),134,10.960663313432837,3.9096921347220377,0.017540880597014925,0.00750323608895778

src/gui/work_dir/6_75_custom_regression/394.450012_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,394.450012,0.0016938639111105935,y = 0.015234 + 0.000830*ln(x),134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202
-linear,Chlorophyll,394.450012,0.0010649475553690113,y = 0.016503 + 0.000061*x,134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202
-power,Chlorophyll,394.450012,-0.023745377413006752,y = 0.020435 * x^-0.094840,134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202
-exponential,Chlorophyll,394.450012,-0.02617627918721488,y = 0.018415 * exp(-0.010666*x),134,10.960663313432837,3.9096921347220377,0.01716890298507463,0.007280847323239202

src/gui/work_dir/6_75_custom_regression/398.493011_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,398.493011,0.000857763974499659,y = 0.015092 + 0.000573*ln(x),134,10.960663313432837,3.9096921347220377,0.016425865671641792,0.00705544097312418
-linear,Chlorophyll,398.493011,0.0003890186261535922,y = 0.016036 + 0.000036*x,134,10.960663313432837,3.9096921347220377,0.016425865671641792,0.00705544097312418

src/gui/work_dir/6_75_custom_regression/402.539001_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,402.539001,0.00048016503667658306,y = 0.015505 + 0.000443*ln(x),134,10.960663313432837,3.9096921347220377,0.01653574626865672,0.007288381669035372
-linear,Chlorophyll,402.539001,0.0001313248786827259,y = 0.016302 + 0.000021*x,134,10.960663313432837,3.9096921347220377,0.01653574626865672,0.007288381669035372

src/gui/work_dir/6_75_custom_regression/406.588989_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,406.588989,0.00016428918964617178,y = 0.015242 + 0.000242*ln(x),134,10.960663313432837,3.9096921347220377,0.015806723880597017,0.006825478500958131
-linear,Chlorophyll,406.588989,1.6937017762730378e-06,y = 0.015782 + 0.000002*x,134,10.960663313432837,3.9096921347220377,0.015806723880597017,0.006825478500958131

src/gui/work_dir/6_75_custom_regression/410.641998_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,410.641998,0.00010695507791136372,y = 0.014822 + 0.000189*ln(x),134,10.960663313432837,3.9096921347220377,0.015261298507462688,0.0065848636688817614
-linear,Chlorophyll,410.641998,2.2039578226884515e-06,y = 0.015289 + -0.000003*x,134,10.960663313432837,3.9096921347220377,0.015261298507462688,0.0065848636688817614

src/gui/work_dir/6_75_custom_regression/414.699005_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,414.699005,9.23718683270014e-05,y = 0.014978 + -0.000015*x,134,10.960663313432837,3.9096921347220377,0.014808014925373135,0.006298696608817295
-logarithmic,Chlorophyll,414.699005,1.0794055052776308e-05,y = 0.014674 + 0.000057*ln(x),134,10.960663313432837,3.9096921347220377,0.014808014925373135,0.006298696608817295

src/gui/work_dir/6_75_custom_regression/418.759003_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,418.759003,0.0002645667637589666,y = 0.014364 + -0.000025*x,134,10.960663313432837,3.9096921347220377,0.014088052238805972,0.006051440804527788
-logarithmic,Chlorophyll,418.759003,7.794051727350038e-06,y = 0.014197 + -0.000047*ln(x),134,10.960663313432837,3.9096921347220377,0.014088052238805972,0.006051440804527788

src/gui/work_dir/6_75_custom_regression/422.821991_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,422.821991,0.0008115657894584016,y = 0.014105 + -0.000042*x,134,10.960663313432837,3.9096921347220377,0.013641977611940298,0.005799322545956216
-logarithmic,Chlorophyll,422.821991,0.0001768579797475356,y = 0.014140 + -0.000214*ln(x),134,10.960663313432837,3.9096921347220377,0.013641977611940298,0.005799322545956216

src/gui/work_dir/6_75_custom_regression/426.889008_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,426.889008,0.0013073210454338513,y = 0.014170 + -0.000053*x,134,10.960663313432837,3.9096921347220377,0.013589074626865672,0.005728319043930576
-logarithmic,Chlorophyll,426.889008,0.0004182937928386421,y = 0.014345 + -0.000325*ln(x),134,10.960663313432837,3.9096921347220377,0.013589074626865672,0.005728319043930576

src/gui/work_dir/6_75_custom_regression/430.959015_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,430.959015,0.002347137019542922,y = 0.013733 + -0.000067*x,134,10.960663313432837,3.9096921347220377,0.012997753731343284,0.005410808768465578
-logarithmic,Chlorophyll,430.959015,0.0010658686661263461,y = 0.014138 + -0.000489*ln(x),134,10.960663313432837,3.9096921347220377,0.012997753731343284,0.005410808768465578

src/gui/work_dir/6_75_custom_regression/435.032013_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,435.032013,0.0024283085040497365,y = 0.013179 + -0.000069*x,134,10.960663313432837,3.9096921347220377,0.012427850746268653,0.005435180040005626
-logarithmic,Chlorophyll,435.032013,0.0011266238526388417,y = 0.013606 + -0.000506*ln(x),134,10.960663313432837,3.9096921347220377,0.012427850746268653,0.005435180040005626

src/gui/work_dir/6_75_custom_regression/439.109009_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,439.109009,0.0042064615418079265,y = 0.013397 + -0.000086*x,134,10.960663313432837,3.9096921347220377,0.012450895522388062,0.005205005715323194
-logarithmic,Chlorophyll,439.109009,0.002294686396103418,y = 0.014061 + -0.000691*ln(x),134,10.960663313432837,3.9096921347220377,0.012450895522388062,0.005205005715323194

src/gui/work_dir/6_75_custom_regression/443.190002_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,443.190002,0.004695213661859765,y = 0.013279 + -0.000091*x,134,10.960663313432837,3.9096921347220377,0.012285432835820896,0.00517286197733264
-logarithmic,Chlorophyll,443.190002,0.0026235944054925353,y = 0.013996 + -0.000734*ln(x),134,10.960663313432837,3.9096921347220377,0.012285432835820896,0.00517286197733264

src/gui/work_dir/6_75_custom_regression/447.27301_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,447.27301,0.005169120886448608,y = 0.013250 + -0.000094*x,134,10.960663313432837,3.9096921347220377,0.012221335820895522,0.005101097814158477
-logarithmic,Chlorophyll,447.27301,0.002968315833349222,y = 0.014016 + -0.000770*ln(x),134,10.960663313432837,3.9096921347220377,0.012221335820895522,0.005101097814158477

src/gui/work_dir/6_75_custom_regression/451.360992_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,451.360992,0.004863772601295668,y = 0.013262 + -0.000092*x,134,10.960663313432837,3.9096921347220377,0.012257104477611941,0.00513769471108476
-logarithmic,Chlorophyll,451.360992,0.002739591016255649,y = 0.013993 + -0.000745*ln(x),134,10.960663313432837,3.9096921347220377,0.012257104477611941,0.00513769471108476

src/gui/work_dir/6_75_custom_regression/455.450989_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,455.450989,0.005525011318207929,y = 0.013206 + -0.000097*x,134,10.960663313432837,3.9096921347220377,0.012144328358208955,0.005093595896892254
-logarithmic,Chlorophyll,455.450989,0.0032249891993542112,y = 0.014012 + -0.000802*ln(x),134,10.960663313432837,3.9096921347220377,0.012144328358208955,0.005093595896892254

src/gui/work_dir/6_75_custom_regression/459.545013_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,459.545013,0.005303969126716268,y = 0.013029 + -0.000095*x,134,10.960663313432837,3.9096921347220377,0.011992097014925374,0.005076948054716495
-logarithmic,Chlorophyll,459.545013,0.0030599507272712767,y = 0.013805 + -0.000778*ln(x),134,10.960663313432837,3.9096921347220377,0.011992097014925374,0.005076948054716495

src/gui/work_dir/6_75_custom_regression/463.641998_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,463.641998,0.005309017626029533,y = 0.013098 + -0.000096*x,134,10.960663313432837,3.9096921347220377,0.012043410447761194,0.0051616384058770694
-logarithmic,Chlorophyll,463.641998,0.00309369061071596,y = 0.013897 + -0.000796*ln(x),134,10.960663313432837,3.9096921347220377,0.012043410447761194,0.0051616384058770694

src/gui/work_dir/6_75_custom_regression/467.743011_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,467.743011,0.005910540579640466,y = 0.013129 + -0.000101*x,134,10.960663313432837,3.9096921347220377,0.012021395522388062,0.0051373056777988335
-logarithmic,Chlorophyll,467.743011,0.0035312797033351107,y = 0.013992 + -0.000846*ln(x),134,10.960663313432837,3.9096921347220377,0.012021395522388062,0.0051373056777988335

src/gui/work_dir/6_75_custom_regression/471.846985_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,471.846985,0.006597953952974689,y = 0.013090 + -0.000107*x,134,10.960663313432837,3.9096921347220377,0.011916462686567163,0.005154508155317495
-logarithmic,Chlorophyll,471.846985,0.004055554987167143,y = 0.014036 + -0.000910*ln(x),134,10.960663313432837,3.9096921347220377,0.011916462686567163,0.005154508155317495

src/gui/work_dir/6_75_custom_regression/475.954987_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,475.954987,0.006969427568661812,y = 0.013222 + -0.000110*x,134,10.960663313432837,3.9096921347220377,0.012010992537313433,0.005173190365687869
-logarithmic,Chlorophyll,475.954987,0.0043493646432547495,y = 0.014214 + -0.000945*ln(x),134,10.960663313432837,3.9096921347220377,0.012010992537313433,0.005173190365687869

src/gui/work_dir/6_75_custom_regression/480.065002_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,480.065002,0.006908555626254476,y = 0.013165 + -0.000112*x,134,10.960663313432837,3.9096921347220377,0.011938738805970149,0.005260977465686793
-logarithmic,Chlorophyll,480.065002,0.004358699372388197,y = 0.014181 + -0.000962*ln(x),134,10.960663313432837,3.9096921347220377,0.011938738805970149,0.005260977465686793

src/gui/work_dir/6_75_custom_regression/484.179993_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,484.179993,0.007786080902936532,y = 0.013479 + -0.000120*x,134,10.960663313432837,3.9096921347220377,0.01216315671641791,0.00531894585260112
-logarithmic,Chlorophyll,484.179993,0.00503315433665652,y = 0.014599 + -0.001046*ln(x),134,10.960663313432837,3.9096921347220377,0.01216315671641791,0.00531894585260112

src/gui/work_dir/6_75_custom_regression/488.296997_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,488.296997,0.009003426521211222,y = 0.013413 + -0.000127*x,134,10.960663313432837,3.9096921347220377,0.01202031343283582,0.005235852382412684
-logarithmic,Chlorophyll,488.296997,0.0059888617317502835,y = 0.014636 + -0.001123*ln(x),134,10.960663313432837,3.9096921347220377,0.01202031343283582,0.005235852382412684

src/gui/work_dir/6_75_custom_regression/492.417999_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,492.417999,0.009356273796731096,y = 0.013426 + -0.000130*x,134,10.960663313432837,3.9096921347220377,0.011996694029850746,0.00526918646504346
-logarithmic,Chlorophyll,492.417999,0.0063786563113004124,y = 0.014714 + -0.001166*ln(x),134,10.960663313432837,3.9096921347220377,0.011996694029850746,0.00526918646504346

src/gui/work_dir/6_75_custom_regression/496.542999_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,496.542999,0.008454580487572083,y = 0.013491 + -0.000126*x,134,10.960663313432837,3.9096921347220377,0.012105947761194029,0.00537461785981684
-logarithmic,Chlorophyll,496.542999,0.005612309162053686,y = 0.014705 + -0.001116*ln(x),134,10.960663313432837,3.9096921347220377,0.012105947761194029,0.00537461785981684

src/gui/work_dir/6_75_custom_regression/500.67099_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,500.67099,0.008930402290994066,y = 0.013947 + -0.000130*x,134,10.960663313432837,3.9096921347220377,0.012519723880597015,0.005386983290859261
-logarithmic,Chlorophyll,500.67099,0.006027433290726858,y = 0.015220 + -0.001159*ln(x),134,10.960663313432837,3.9096921347220377,0.012519723880597015,0.005386983290859261

src/gui/work_dir/6_75_custom_regression/504.802002_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,504.802002,0.007851805368295328,y = 0.014159 + -0.000122*x,134,10.960663313432837,3.9096921347220377,0.012821291044776119,0.005386616665575906
-logarithmic,Chlorophyll,504.802002,0.005168584239575225,y = 0.015321 + -0.001073*ln(x),134,10.960663313432837,3.9096921347220377,0.012821291044776119,0.005386616665575906

src/gui/work_dir/6_75_custom_regression/508.936005_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,508.936005,0.006500148302153508,y = 0.014525 + -0.000113*x,134,10.960663313432837,3.9096921347220377,0.013285626865671642,0.005484303548583865
-logarithmic,Chlorophyll,508.936005,0.004158811025431031,y = 0.015569 + -0.000980*ln(x),134,10.960663313432837,3.9096921347220377,0.013285626865671642,0.005484303548583865

src/gui/work_dir/6_75_custom_regression/513.073975_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,513.073975,0.004917130582781315,y = 0.014969 + -0.000099*x,134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309
-logarithmic,Chlorophyll,513.073975,0.0029641857181392783,y = 0.015828 + -0.000836*ln(x),134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309
-exponential,Chlorophyll,513.073975,-0.01889032090850251,y = 0.016669 * exp(-0.020406*x),134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309
-power,Chlorophyll,513.073975,-0.019715344472010177,y = 0.020892 * x^-0.192919,134,10.960663313432837,3.9096921347220377,0.013879231343283581,0.005544190630289309

src/gui/work_dir/6_75_custom_regression/517.216003_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,517.216003,0.004276559761211218,y = 0.015763 + -0.000093*x,134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703
-logarithmic,Chlorophyll,517.216003,0.00245772439289782,y = 0.016520 + -0.000761*ln(x),134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703
-exponential,Chlorophyll,517.216003,-0.018892619920322984,y = 0.017382 * exp(-0.018434*x),134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703
-power,Chlorophyll,517.216003,-0.0194484153066794,y = 0.021251 * x^-0.172972,134,10.960663313432837,3.9096921347220377,0.014746992537313432,0.005540761628631703

src/gui/work_dir/6_75_custom_regression/521.361023_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,521.361023,0.002867645407956476,y = 0.015976 + -0.000077*x,134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285
-logarithmic,Chlorophyll,521.361023,0.001510103149844122,y = 0.016540 + -0.000602*ln(x),134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285
-exponential,Chlorophyll,521.361023,-0.013492164822140218,y = 0.017293 * exp(-0.014959*x),134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285
-power,Chlorophyll,521.361023,-0.01389692075440152,y = 0.020293 * x^-0.139034,134,10.960663313432837,3.9096921347220377,0.01513621641791045,0.0055935618166287285

src/gui/work_dir/6_75_custom_regression/525.508972_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,525.508972,0.0016701377663735917,y = 0.016584 + -0.000059*x,134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037
-logarithmic,Chlorophyll,525.508972,0.0007153098124390578,y = 0.016913 + -0.000419*ln(x),134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037
-exponential,Chlorophyll,525.508972,-0.011873247394506237,y = 0.017736 * exp(-0.012223*x),134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037
-power,Chlorophyll,525.508972,-0.01195937275095682,y = 0.020122 * x^-0.111667,134,10.960663313432837,3.9096921347220377,0.015935962686567166,0.005657732827939037

src/gui/work_dir/6_75_custom_regression/529.659973_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,529.659973,0.001100615217659584,y = 0.016897 + -0.000048*x,134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815
-logarithmic,Chlorophyll,529.659973,0.00038742510141320796,y = 0.017089 + -0.000308*ln(x),134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815
-power,Chlorophyll,529.659973,-0.010602748616709512,y = 0.019984 * x^-0.095978,134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815
-exponential,Chlorophyll,529.659973,-0.010676230097632189,y = 0.017950 * exp(-0.010610*x),134,10.960663313432837,3.9096921347220377,0.016371305970149252,0.005647344987370815

src/gui/work_dir/6_75_custom_regression/533.815002_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,533.815002,0.0005274884679772329,y = 0.017331 + -0.000034*x,134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051
-logarithmic,Chlorophyll,533.815002,9.639796014881963e-05,y = 0.017325 + -0.000156*ln(x),134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051
-power,Chlorophyll,533.815002,-0.010942625292486463,y = 0.020200 * x^-0.085250,134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051
-exponential,Chlorophyll,533.815002,-0.011375297319334177,y = 0.018394 * exp(-0.009577*x),134,10.960663313432837,3.9096921347220377,0.016960171641791044,0.005752288800181051

src/gui/work_dir/6_75_custom_regression/537.973999_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,537.973999,0.010059827315010317,y = 0.018287 + -0.000095*x,134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316
-logarithmic,Chlorophyll,537.973999,0.0058263736903666485,y = 0.019072 + -0.000784*ln(x),134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316
-exponential,Chlorophyll,537.973999,-0.0006317634379562342,y = 0.018947 * exp(-0.009908*x),134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316
-power,Chlorophyll,537.973999,-0.004088770616538007,y = 0.020915 * x^-0.089031,134,10.960663313432837,3.9096921347220377,0.017245291044776116,0.003706413209287316

src/gui/work_dir/6_75_custom_regression/542.13501_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,542.13501,0.10592938275035968,y = 0.019264 + -0.000164*x,134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118
-exponential,Chlorophyll,542.13501,0.09468361275300774,y = 0.019618 * exp(-0.011250*x),134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118
-logarithmic,Chlorophyll,542.13501,0.07410535895090076,y = 0.020924 + -0.001482*ln(x),134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118
-power,Chlorophyll,542.13501,0.06336507350101761,y = 0.022043 * x^-0.102942,134,10.960663313432837,3.9096921347220377,0.017471380597014925,0.0019645221126944118

src/gui/work_dir/6_75_custom_regression/546.301025_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,546.301025,0.12040522641720053,y = 0.019716 + -0.000169*x,134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596
-exponential,Chlorophyll,546.301025,0.11002711959899414,y = 0.020039 * exp(-0.011103*x),134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596
-logarithmic,Chlorophyll,546.301025,0.08587624549622919,y = 0.021467 + -0.001547*ln(x),134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596
-power,Chlorophyll,546.301025,0.07589213783045401,y = 0.022516 * x^-0.102241,134,10.960663313432837,3.9096921347220377,0.01786192537313433,0.0019057641301598596

src/gui/work_dir/6_75_custom_regression/550.468994_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,550.468994,0.1269569921216488,y = 0.020121 + -0.000172*x,134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905
-exponential,Chlorophyll,550.468994,0.1175391734287099,y = 0.020411 * exp(-0.010816*x),134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905
-logarithmic,Chlorophyll,550.468994,0.0907980339917791,y = 0.021903 + -0.001572*ln(x),134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905
-power,Chlorophyll,550.468994,0.08162875081638976,y = 0.022867 * x^-0.099639,134,10.960663313432837,3.9096921347220377,0.01824102985074627,0.0018825013010254905

src/gui/work_dir/6_75_custom_regression/554.640991_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,554.640991,0.1376426262180579,y = 0.020508 + -0.000178*x,134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957
-exponential,Chlorophyll,554.640991,0.12753098839026022,y = 0.020816 * exp(-0.011054*x),134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957
-logarithmic,Chlorophyll,554.640991,0.09810495477002423,y = 0.022354 + -0.001631*ln(x),134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957
-power,Chlorophyll,554.640991,0.08828430738543391,y = 0.023368 * x^-0.101637,134,10.960663313432837,3.9096921347220377,0.018552656716417912,0.0018796053138591957

src/gui/work_dir/6_75_custom_regression/558.815979_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,558.815979,0.14438886343879165,y = 0.020984 + -0.000192*x,134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088
-exponential,Chlorophyll,558.815979,0.13400466793505816,y = 0.021319 * exp(-0.011710*x),134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088
-logarithmic,Chlorophyll,558.815979,0.10606464763119816,y = 0.023038 + -0.001786*ln(x),134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088
-power,Chlorophyll,558.815979,0.09560595602330613,y = 0.024181 * x^-0.109152,134,10.960663313432837,3.9096921347220377,0.018875544776119402,0.0019794547666891088

src/gui/work_dir/6_75_custom_regression/562.994995_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,562.994995,0.13570684972920022,y = 0.021065 + -0.000193*x,134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121
-exponential,Chlorophyll,562.994995,0.1251679621881412,y = 0.021424 * exp(-0.011845*x),134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121
-logarithmic,Chlorophyll,562.994995,0.10005264191024388,y = 0.023135 + -0.001797*ln(x),134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121
-power,Chlorophyll,562.994995,0.08944628510214314,y = 0.024352 * x^-0.110685,134,10.960663313432837,3.9096921347220377,0.018946902985074628,0.002050816811679121

src/gui/work_dir/6_75_custom_regression/567.177002_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,567.177002,0.03705163102869502,y = 0.020812 + -0.000155*x,134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114
-exponential,Chlorophyll,567.177002,0.02735476743599441,y = 0.021355 * exp(-0.011096*x),134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114
-logarithmic,Chlorophyll,567.177002,0.02624551679688847,y = 0.022403 + -0.001411*ln(x),134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114
-power,Chlorophyll,567.177002,0.01690050625981776,y = 0.024064 * x^-0.103446,134,10.960663313432837,3.9096921347220377,0.01911655970149254,0.0031426043067057114

src/gui/work_dir/6_75_custom_regression/571.362976_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,571.362976,0.032367014815650186,y = 0.020732 + -0.000161*x,134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925
-logarithmic,Chlorophyll,571.362976,0.023243518566287924,y = 0.022412 + -0.001479*ln(x),134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925
-exponential,Chlorophyll,571.362976,0.021156108573575194,y = 0.021404 * exp(-0.012237*x),134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925
-power,Chlorophyll,571.362976,0.012371457852355827,y = 0.024473 * x^-0.115076,134,10.960663313432837,3.9096921347220377,0.018965664179104478,0.0035018296559745925

src/gui/work_dir/6_75_custom_regression/575.551025_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,575.551025,0.003711200606445142,y = 0.019973 + -0.000095*x,134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235
-logarithmic,Chlorophyll,575.551025,0.0022851664540198824,y = 0.020813 + -0.000808*ln(x),134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235
-exponential,Chlorophyll,575.551025,-0.008535989595423121,y = 0.021158 * exp(-0.012309*x),134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235
-power,Chlorophyll,575.551025,-0.009305973499541542,y = 0.024221 * x^-0.115919,134,10.960663313432837,3.9096921347220377,0.018931119402985072,0.006097893443512235

src/gui/work_dir/6_75_custom_regression/579.744019_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,579.744019,0.005094509026133509,y = 0.019711 + -0.000110*x,134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274
-logarithmic,Chlorophyll,579.744019,0.0034065768371064342,y = 0.020776 + -0.000974*ln(x),134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274
-exponential,Chlorophyll,579.744019,-0.007270136129956084,y = 0.020909 * exp(-0.013357*x),134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274
-power,Chlorophyll,579.744019,-0.008400608136997167,y = 0.024296 * x^-0.127275,134,10.960663313432837,3.9096921347220377,0.018505432835820897,0.006025405615191274

src/gui/work_dir/6_75_custom_regression/583.939026_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,583.939026,0.004657425272328819,y = 0.019070 + -0.000108*x,134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186
-logarithmic,Chlorophyll,583.939026,0.003161690862374833,y = 0.020137 + -0.000968*ln(x),134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186
-exponential,Chlorophyll,583.939026,-0.00834323144293947,y = 0.020355 * exp(-0.014251*x),134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186
-power,Chlorophyll,583.939026,-0.009289166222129941,y = 0.023939 * x^-0.136644,134,10.960663313432837,3.9096921347220377,0.017881,0.006215784305887186

src/gui/work_dir/6_75_custom_regression/588.138_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,588.138,0.007038900978221574,y = 0.018745 + -0.000132*x,134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735
-logarithmic,Chlorophyll,588.138,0.005079658860641656,y = 0.020131 + -0.001218*ln(x),134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735
-exponential,Chlorophyll,588.138,-0.008087990598099282,y = 0.020176 * exp(-0.016777*x),134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735
-power,Chlorophyll,588.138,-0.009554552395312665,y = 0.024503 * x^-0.162327,134,10.960663313432837,3.9096921347220377,0.017294746268656718,0.006164975937402735

src/gui/work_dir/6_75_custom_regression/592.341003_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,592.341003,0.005528079261697849,y = 0.017838 + -0.000118*x,134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627
-logarithmic,Chlorophyll,592.341003,0.003887007195125136,y = 0.019044 + -0.001073*ln(x),134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627
-exponential,Chlorophyll,592.341003,-0.011813537096786675,y = 0.019386 * exp(-0.017528*x),134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627
-power,Chlorophyll,592.341003,-0.012846809152319283,y = 0.023741 * x^-0.169433,134,10.960663313432837,3.9096921347220377,0.016543097014925373,0.006212725389009627

src/gui/work_dir/6_75_custom_regression/596.546997_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,596.546997,0.003794780064093062,y = 0.016713 + -0.000101*x,134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859
-logarithmic,Chlorophyll,596.546997,0.0025823661966439815,y = 0.017707 + -0.000901*ln(x),134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859
-exponential,Chlorophyll,596.546997,-0.014874157713247849,y = 0.018368 * exp(-0.018321*x),134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859
-power,Chlorophyll,596.546997,-0.015374647488444415,y = 0.022702 * x^-0.177115,134,10.960663313432837,3.9096921347220377,0.015607044776119405,0.00640171329313859

src/gui/work_dir/6_75_custom_regression/600.755981_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,600.755981,0.0031611200601100453,y = 0.015616 + -0.000092*x,134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828
-logarithmic,Chlorophyll,600.755981,0.002151138099984129,y = 0.016523 + -0.000822*ln(x),134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828
-exponential,Chlorophyll,600.755981,-0.015732414466808953,y = 0.017258 * exp(-0.018983*x),134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828
-power,Chlorophyll,600.755981,-0.016109249860633446,y = 0.021535 * x^-0.184339,134,10.960663313432837,3.9096921347220377,0.014608059701492537,0.006395783738002828

src/gui/work_dir/6_75_custom_regression/604.968994_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,604.968994,0.002398045183009123,y = 0.014883 + -0.000082*x,134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055
-logarithmic,Chlorophyll,604.968994,0.001603534119833716,y = 0.015675 + -0.000725*ln(x),134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055
-power,Chlorophyll,604.968994,-0.019789876685375907,y = 0.021361 * x^-0.202172,134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055
-exponential,Chlorophyll,604.968994,-0.019848091450597183,y = 0.016749 * exp(-0.020787*x),134,10.960663313432837,3.9096921347220377,0.013985902985074627,0.0065333725432655055

src/gui/work_dir/6_75_custom_regression/609.184998_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,609.184998,0.0026364151647212397,y = 0.014491 + -0.000087*x,134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493
-logarithmic,Chlorophyll,609.184998,0.001802623235843237,y = 0.015351 + -0.000777*ln(x),134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493
-exponential,Chlorophyll,609.184998,-0.02173372426195974,y = 0.016508 * exp(-0.022851*x),134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493
-power,Chlorophyll,609.184998,-0.021740201254617064,y = 0.021605 * x^-0.222988,134,10.960663313432837,3.9096921347220377,0.013541022388059702,0.006602440257226493

src/gui/work_dir/6_75_custom_regression/613.403992_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,613.403992,0.002856835284101855,y = 0.014122 + -0.000091*x,134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487
-logarithmic,Chlorophyll,613.403992,0.0019870205661061124,y = 0.015038 + -0.000820*ln(x),134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487
-power,Chlorophyll,613.403992,-0.028809677197195516,y = 0.022780 * x^-0.263141,134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487
-exponential,Chlorophyll,613.403992,-0.02909727099411552,y = 0.016581 * exp(-0.026956*x),134,10.960663313432837,3.9096921347220377,0.013127970149253732,0.006635524455917487

src/gui/work_dir/6_75_custom_regression/617.627014_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,617.627014,0.0029411042399954956,y = 0.013748 + -0.000092*x,134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145
-logarithmic,Chlorophyll,617.627014,0.0020597681456059336,y = 0.014681 + -0.000832*ln(x),134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145
-power,Chlorophyll,617.627014,-0.02980562740210102,y = 0.022702 * x^-0.275804,134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145
-exponential,Chlorophyll,617.627014,-0.030009751236651283,y = 0.016267 * exp(-0.028218*x),134,10.960663313432837,3.9096921347220377,0.012741731343283583,0.006619138380331145

src/gui/work_dir/6_75_custom_regression/621.853027_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,621.853027,0.002549517816160085,y = 0.013405 + -0.000087*x,134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926
-logarithmic,Chlorophyll,621.853027,0.0017633821329291477,y = 0.014281 + -0.000787*ln(x),134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926
-power,Chlorophyll,621.853027,-0.03557395594809787,y = 0.023522 * x^-0.304801,134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926
-exponential,Chlorophyll,621.853027,-0.0360883361973503,y = 0.016274 * exp(-0.031185*x),134,10.960663313432837,3.9096921347220377,0.012447716417910447,0.006761993519104926

src/gui/work_dir/6_75_custom_regression/626.083008_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,626.083008,0.00243086769672618,y = 0.013228 + -0.000086*x,134,10.960663313432837,3.9096921347220377,0.01228736567164179,0.006808151575409212
-logarithmic,Chlorophyll,626.083008,0.0016770114876123454,y = 0.014087 + -0.000773*ln(x),134,10.960663313432837,3.9096921347220377,0.01228736567164179,0.006808151575409212

src/gui/work_dir/6_75_custom_regression/630.315979_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,630.315979,0.0020507276873319435,y = 0.013084 + -0.000080*x,134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635
-logarithmic,Chlorophyll,630.315979,0.0014098383321846653,y = 0.013886 + -0.000723*ln(x),134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635
-power,Chlorophyll,630.315979,-0.037502977238619506,y = 0.023711 * x^-0.319128,134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635
-exponential,Chlorophyll,630.315979,-0.03824118768398388,y = 0.016114 * exp(-0.032602*x),134,10.960663313432837,3.9096921347220377,0.012202574626865673,0.0069447202176291635

src/gui/work_dir/6_75_custom_regression/634.552002_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,634.552002,0.0017288451024077833,y = 0.012968 + -0.000074*x,134,10.960663313432837,3.9096921347220377,0.012159492537313433,0.006937426491471543
-logarithmic,Chlorophyll,634.552002,0.0011589143811774338,y = 0.013684 + -0.000654*ln(x),134,10.960663313432837,3.9096921347220377,0.012159492537313433,0.006937426491471543

src/gui/work_dir/6_75_custom_regression/638.791992_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,638.791992,0.0011243091081014622,y = 0.012803 + -0.000061*x,134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063
-logarithmic,Chlorophyll,638.791992,0.0007004467304244644,y = 0.013348 + -0.000520*ln(x),134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063
-power,Chlorophyll,638.791992,-0.039341503691473934,y = 0.023270 * x^-0.314552,134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063
-exponential,Chlorophyll,638.791992,-0.040722297966500065,y = 0.015930 * exp(-0.032288*x),134,10.960663313432837,3.9096921347220377,0.012136574626865673,0.007088601297076063

src/gui/work_dir/6_75_custom_regression/643.034973_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,643.034973,0.0006784732036110297,y = 0.012527 + -0.000048*x,134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512
-logarithmic,Chlorophyll,643.034973,0.0003810729347989428,y = 0.012907 + -0.000387*ln(x),134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512
-power,Chlorophyll,643.034973,-0.02081846389883313,y = 0.018994 * x^-0.223479,134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512
-exponential,Chlorophyll,643.034973,-0.02135362421636633,y = 0.014510 * exp(-0.022936*x),134,10.960663313432837,3.9096921347220377,0.012004380597014924,0.007158811687218512

src/gui/work_dir/6_75_custom_regression/647.281006_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,647.281006,0.0003862335627651259,y = 0.012126 + -0.000037*x,134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486
-logarithmic,Chlorophyll,647.281006,0.00018807939298448595,y = 0.012369 + -0.000276*ln(x),134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486
-power,Chlorophyll,647.281006,-0.01877601035356702,y = 0.017959 * x^-0.209287,134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486
-exponential,Chlorophyll,647.281006,-0.019328048239278806,y = 0.013958 * exp(-0.021494*x),134,10.960663313432837,3.9096921347220377,0.01172576119402985,0.007268962283181486

src/gui/work_dir/6_75_custom_regression/651.531006_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,651.531006,0.00013536784274881253,y = 0.011403 + -0.000022*x,134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625
-logarithmic,Chlorophyll,651.531006,3.4274085199070825e-05,y = 0.011441 + -0.000119*ln(x),134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625
-power,Chlorophyll,651.531006,-0.01999886130480988,y = 0.017201 * x^-0.213892,134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625
-exponential,Chlorophyll,651.531006,-0.02080973971468425,y = 0.013316 * exp(-0.022113*x),134,10.960663313432837,3.9096921347220377,0.011164671641791043,0.007310371385073625

src/gui/work_dir/6_75_custom_regression/655.784973_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,655.784973,6.712523797403058e-05,y = 0.010412 + -0.000016*x,134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311
-logarithmic,Chlorophyll,655.784973,7.939365778253382e-06,y = 0.010377 + -0.000058*ln(x),134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311
-power,Chlorophyll,655.784973,-0.02451200929710562,y = 0.017204 * x^-0.257829,134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311
-exponential,Chlorophyll,655.784973,-0.025546357658017715,y = 0.012631 * exp(-0.026616*x),134,10.960663313432837,3.9096921347220377,0.010241910447761193,0.007414076922911311

src/gui/work_dir/6_75_custom_regression/660.041016_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,660.041016,2.3065421406842646e-05,y = 0.009045 + -0.000009*x,134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144
-logarithmic,Chlorophyll,660.041016,8.478475440609756e-07,y = 0.008902 + 0.000019*ln(x),134,10.960663313432837,3.9096921347220377,0.008946089552238806,0.007372050533373144

src/gui/work_dir/6_75_custom_regression/664.302002_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,664.302002,2.70996016560332e-05,y = 0.008101 + -0.000010*x,134,10.960663313432837,3.9096921347220377,0.007993440298507463,0.007397490717674307
-logarithmic,Chlorophyll,664.302002,4.148671942649784e-07,y = 0.007963 + 0.000013*ln(x),134,10.960663313432837,3.9096921347220377,0.007993440298507463,0.007397490717674307

src/gui/work_dir/6_75_custom_regression/668.565002_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,668.565002,9.040046325403672e-06,y = 0.007498 + -0.000006*x,134,10.960663313432837,3.9096921347220377,0.0074355522388059704,0.007438585861600675
-logarithmic,Chlorophyll,668.565002,8.725061217407237e-06,y = 0.007294 + 0.000061*ln(x),134,10.960663313432837,3.9096921347220377,0.0074355522388059704,0.007438585861600675

src/gui/work_dir/6_75_custom_regression/672.83197_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,672.83197,2.8342079669063658e-05,y = 0.007006 + 0.000109*ln(x),134,10.960663313432837,3.9096921347220377,0.007259574626865672,0.0073841624312846075
-linear,Chlorophyll,672.83197,2.942331951416577e-07,y = 0.007271 + -0.000001*x,134,10.960663313432837,3.9096921347220377,0.007259574626865672,0.0073841624312846075

src/gui/work_dir/6_75_custom_regression/677.10199_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,677.10199,7.905307206101941e-05,y = 0.007009 + 0.000182*ln(x),134,10.960663313432837,3.9096921347220377,0.007434537313432836,0.007405361783473518
-linear,Chlorophyll,677.10199,8.391699477305892e-06,y = 0.007374 + 0.000005*x,134,10.960663313432837,3.9096921347220377,0.007434537313432836,0.007405361783473518

src/gui/work_dir/6_75_custom_regression/681.375977_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,681.375977,0.00035444194765099635,y = 0.007047 + 0.000397*ln(x),134,10.960663313432837,3.9096921347220377,0.007971380597014925,0.007601910615882189
-linear,Chlorophyll,681.375977,0.00018023567598490775,y = 0.007685 + 0.000026*x,134,10.960663313432837,3.9096921347220377,0.007971380597014925,0.007601910615882189

src/gui/work_dir/6_75_custom_regression/685.653015_regression_results.csv

@@ -1,3 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,685.653015,0.0012936127446737666,y = 0.007106 + 0.000788*ln(x),134,10.960663313432837,3.9096921347220377,0.00894305223880597,0.007911710832888247
-linear,Chlorophyll,685.653015,0.0010226571268574514,y = 0.008234 + 0.000065*x,134,10.960663313432837,3.9096921347220377,0.00894305223880597,0.007911710832888247

src/gui/work_dir/6_75_custom_regression/689.932983_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,689.932983,0.004302680997953723,y = 0.006459 + 0.001505*ln(x),134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581
-linear,Chlorophyll,689.932983,0.0041204289706771036,y = 0.008475 + 0.000136*x,134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581
-power,Chlorophyll,689.932983,-0.011112637221193156,y = 0.010691 * x^-0.061728,134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581
-exponential,Chlorophyll,689.932983,-0.012347372143508784,y = 0.010039 * exp(-0.007382*x),134,10.960663313432837,3.9096921347220377,0.009964873134328357,0.008279228255026581

src/gui/work_dir/6_75_custom_regression/694.21698_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,694.21698,0.009659788549096326,y = 0.007405 + 0.001260*ln(x),134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055
-linear,Chlorophyll,694.21698,0.008988938182027284,y = 0.009111 + 0.000112*x,134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055
-power,Chlorophyll,694.21698,-0.0008894306327071888,y = 0.009130 * x^0.038452,134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055
-exponential,Chlorophyll,694.21698,-0.002278256235976661,y = 0.009711 * exp(0.002541*x),134,10.960663313432837,3.9096921347220377,0.01034108208955224,0.004627698457727055

src/gui/work_dir/6_75_custom_regression/698.505005_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-logarithmic,Chlorophyll,698.505005,0.031440304449184886,y = 0.007932 + 0.000977*ln(x),134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013
-linear,Chlorophyll,698.505005,0.028545786078388646,y = 0.009266 + 0.000086*x,134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013
-power,Chlorophyll,698.505005,0.024301556337671393,y = 0.008426 * x^0.075955,134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013
-exponential,Chlorophyll,698.505005,0.021173949758028887,y = 0.009382 * exp(0.006346*x),134,10.960663313432837,3.9096921347220377,0.010207731343283583,0.0019876983508052013

src/gui/work_dir/6_75_custom_regression/702.794983_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,702.794983,0.06296172218181084,y = 0.008305 + 0.000144*x,134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098
-logarithmic,Chlorophyll,702.794983,0.06154613752993454,y = 0.006284 + 0.001547*ln(x),134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098
-exponential,Chlorophyll,702.794983,0.05333717741162325,y = 0.008583 * exp(0.011176*x),134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098
-power,Chlorophyll,702.794983,0.052353333406120695,y = 0.007280 * x^0.123243,134,10.960663313432837,3.9096921347220377,0.009888313432835822,0.002250167471107098

src/gui/work_dir/6_75_custom_regression/707.088989_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,707.088989,0.0722787565252282,y = 0.007176 + 0.000174*x,134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013
-logarithmic,Chlorophyll,707.088989,0.06798251906890818,y = 0.004820 + 0.001831*ln(x),134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013
-exponential,Chlorophyll,707.088989,0.05918259759570621,y = 0.007631 * exp(0.013614*x),134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013
-power,Chlorophyll,707.088989,0.05571330673697561,y = 0.006287 * x^0.147166,134,10.960663313432837,3.9096921347220377,0.009086067164179106,0.0025340421111067013

src/gui/work_dir/6_75_custom_regression/711.387024_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,711.387024,0.0801501663676073,y = 0.005716 + 0.000199*x,134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074
-logarithmic,Chlorophyll,711.387024,0.07410981134062689,y = 0.003064 + 0.002076*ln(x),134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074
-exponential,Chlorophyll,711.387024,0.06368164013940003,y = 0.006342 * exp(0.016959*x),134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074
-power,Chlorophyll,711.387024,0.05885933871457438,y = 0.005001 * x^0.181749,134,10.960663313432837,3.9096921347220377,0.007900276119402985,0.0027516224396933074

src/gui/work_dir/6_75_custom_regression/715.687012_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,715.687012,0.07646592984102929,y = 0.004316 + 0.000182*x,134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803
-logarithmic,Chlorophyll,715.687012,0.07053737510880576,y = 0.001896 + 0.001896*ln(x),134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803
-exponential,Chlorophyll,715.687012,0.057012898990752126,y = 0.004952 * exp(0.018319*x),134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803
-power,Chlorophyll,715.687012,0.05263846505811587,y = 0.003824 * x^0.197049,134,10.960663313432837,3.9096921347220377,0.00631360447761194,0.0025762009517042803

src/gui/work_dir/6_75_custom_regression/719.992004_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,719.992004,0.07445743675368166,y = 0.003267 + 0.000166*x,134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048
-logarithmic,Chlorophyll,719.992004,0.06836961037451539,y = 0.001065 + 0.001728*ln(x),134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048
-exponential,Chlorophyll,719.992004,0.05296886200774753,y = 0.003871 * exp(0.020260*x),134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048
-power,Chlorophyll,719.992004,0.048528532136251745,y = 0.002911 * x^0.217642,134,10.960663313432837,3.9096921347220377,0.00509136567164179,0.002385295002066048

src/gui/work_dir/6_75_custom_regression/724.299011_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,724.299011,0.06745397553654364,y = 0.002420 + 0.000133*x,134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247
-logarithmic,Chlorophyll,724.299011,0.062493450238378045,y = 0.000642 + 0.001390*ln(x),134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247
-exponential,Chlorophyll,724.299011,0.045936345781994126,y = 0.002921 * exp(0.020727*x),134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247
-power,Chlorophyll,724.299011,0.04270170271520268,y = 0.002169 * x^0.225290,134,10.960663313432837,3.9096921347220377,0.0038816044776119395,0.0020072190423221247

src/gui/work_dir/6_75_custom_regression/728.609985_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,728.609985,0.043742722087426356,y = 0.001822 + 0.000088*x,134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023
-logarithmic,Chlorophyll,728.609985,0.04115482350546895,y = 0.000631 + 0.000926*ln(x),134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023
-exponential,Chlorophyll,728.609985,0.025695766879270887,y = 0.002157 * exp(0.017908*x),134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023
-power,Chlorophyll,728.609985,0.024423938138158685,y = 0.001657 * x^0.197225,134,10.960663313432837,3.9096921347220377,0.0027881044776119405,0.0016469934764511023

src/gui/work_dir/6_75_custom_regression/732.924988_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,732.924988,0.013546034482326164,y = 0.001304 + 0.000072*x,134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667
-logarithmic,Chlorophyll,732.924988,0.013045034204567596,y = 0.000305 + 0.000769*ln(x),134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667
-power,Chlorophyll,732.924988,-0.0016075191011517553,y = 0.001527 * x^0.092327,134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667
-exponential,Chlorophyll,732.924988,-0.002218314899776086,y = 0.001749 * exp(0.007256*x),134,10.960663313432837,3.9096921347220377,0.0020962686567164183,0.002429007647518667

src/gui/work_dir/6_75_custom_regression/737.242004_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,737.242004,0.01274011186937285,y = 0.000153 + 0.000183*x,134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662
-logarithmic,Chlorophyll,737.242004,0.011709594088295416,y = -0.002273 + 0.001903*ln(x),134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662
-power,Chlorophyll,737.242004,-0.004968418579431422,y = 0.001306 * x^0.096424,134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662
-exponential,Chlorophyll,737.242004,-0.0051926295251412125,y = 0.001506 * exp(0.007477*x),134,10.960663313432837,3.9096921347220377,0.0021605000000000005,0.006345526772941662

src/gui/work_dir/6_75_custom_regression/741.564026_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,741.564026,0.01237420528041,y = -0.000778 + 0.000290*x,134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374
-logarithmic,Chlorophyll,741.564026,0.011168399659673534,y = -0.004556 + 0.002987*ln(x),134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374
-power,Chlorophyll,741.564026,-0.006382018366340336,y = 0.001343 * x^0.060248,134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374
-exponential,Chlorophyll,741.564026,-0.006486914714434855,y = 0.001471 * exp(0.004477*x),134,10.960663313432837,3.9096921347220377,0.0024028432835820897,0.010199237979184374

src/gui/work_dir/6_75_custom_regression/745.888_regression_results.csv

@@ -1,5 +0,0 @@
-regression_method,x_variable,y_variable,r_squared,equation,sample_size,x_mean,x_std,y_mean,y_std
-linear,Chlorophyll,745.888,0.01208622807607107,y = -0.000537 + 0.000255*x,134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967
-logarithmic,Chlorophyll,745.888,0.010783256099801686,y = -0.003818 + 0.002607*ln(x),134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967
-exponential,Chlorophyll,745.888,-0.006541603078447977,y = 0.001459 * exp(0.002645*x),134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967
-power,Chlorophyll,745.888,-0.006568012798676248,y = 0.001406 * x^0.028233,134,10.960663313432837,3.9096921347220377,0.0022554402985074623,0.00905887910227967