From 75eccd561cfc148589dd8f0d513c67f68a4aed08 Mon Sep 17 00:00:00 2001
From: Klaus <klaus.boehnlein@tu-dresden.de>
Date: Fri, 13 Oct 2023 11:30:32 +0200
Subject: [PATCH] Add plot script for relative error
---
.../WoodBilayer_ExperimentComparisonError.py | 223 ++++++++++++++++++
.../WoodBilayer_expComparison_r022.py | 134 -----------
.../WoodBilayer_expComparison_r049.py | 136 -----------
3 files changed, 223 insertions(+), 270 deletions(-)
create mode 100644 Plot-Scripts/WoodBilayer_ExperimentComparisonError.py
delete mode 100644 Plot-Scripts/WoodBilayer_expComparison_r022.py
delete mode 100644 Plot-Scripts/WoodBilayer_expComparison_r049.py
diff --git a/Plot-Scripts/WoodBilayer_ExperimentComparisonError.py b/Plot-Scripts/WoodBilayer_ExperimentComparisonError.py
new file mode 100644
index 00000000..0fb0078c
--- /dev/null
+++ b/Plot-Scripts/WoodBilayer_ExperimentComparisonError.py
@@ -0,0 +1,223 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import math
+import os
+import subprocess
+import fileinput
+import re
+import sys
+import matplotlib as mpl
+from mpl_toolkits.mplot3d import Axes3D
+import matplotlib.cm as cm
+import matplotlib.ticker as ticker
+from matplotlib.ticker import MultipleLocator,FormatStrFormatter,MaxNLocator
+import seaborn as sns
+import matplotlib.colors as mcolors
+# ----------------------------------------------------
+# --- Define Plot style:
+# plt.style.use("seaborn-white")
+# plt.style.use("seaborn-pastel")
+# plt.style.use("seaborn-colorblind")
+plt.style.use("seaborn")
+mpl.rcParams['text.usetex'] = True
+mpl.rcParams["font.family"] = "serif"
+mpl.rcParams["font.size"] = "14"
+mpl.rcParams['xtick.bottom'] = True
+mpl.rcParams['xtick.major.size'] = 3
+mpl.rcParams['xtick.minor.size'] = 1.5
+mpl.rcParams['xtick.major.width'] = 0.75
+mpl.rcParams['ytick.left'] = True
+mpl.rcParams['ytick.major.size'] = 3
+mpl.rcParams['ytick.minor.size'] = 1.5
+mpl.rcParams['ytick.major.width'] = 0.75
+
+#Adjust grid:
+mpl.rcParams.update({"axes.grid" : True}) # Add grid
+mpl.rcParams['axes.labelpad'] = 3
+mpl.rcParams['grid.linewidth'] = 0.25
+mpl.rcParams['grid.alpha'] = 0.9 # 0.75
+mpl.rcParams['grid.linestyle'] = '-'
+mpl.rcParams['grid.color'] = 'gray'#'black'
+mpl.rcParams['text.latex.preamble'] = r'\usepackage{amsfonts}' # Makes Use of \mathbb possible.
+# ----------------------------------------------------------------------------------------
+width = 5.79
+height = width / 1.618 # The golden ratio.
+
+
+
+
+#TODO read values from .txt output files....
+
+dataset_numbers = [0, 1, 2, 3, 4 ,5]
+
+for dataset_number in dataset_numbers:
+
+ # fig = plt.figure() #main
+ # fig, (ax1, ax2) = plt.subplots(1,2, figsize=(width,height))
+ # fig, (ax1, ax2) = plt.subplots(1,2)
+
+ fig, ax = plt.subplots(figsize=(width,height))
+
+ # fig = plt.figure(figsize=(10, 4))
+ # subfigs = fig.subfigures(1, 2, wspace=0.07)
+
+ # ax1 = subfigs[0]
+ # ax2 = subfigs[1]
+
+
+ dataset = [
+
+ [ # Dataset Ratio r = 0.12
+ [14.70179844, 13.6246 , 12.42994508, 11.69773413,11.14159987, 9.500670278, 9.005046347] , # input (moisture-content in %)
+ [1.29323308, 1.83458647 , 2.40601504 , 2.76691729 , 3.03759398, 3.81954887 , 4.03007519], # curvature kappa from Simulation
+ [1.140351217, 1.691038688, 2.243918105, 2.595732726, 2.945361006,4.001528043, 4.312080261] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.17
+ [14.75453569,13.71227639,12.54975012,11.83455959,11.29089521,9.620608917,9.101671742], # input (moisture-content in %)
+ [1.26315789, 1.77443609, 2.34586466, 2.70676692, 2.97744361, 3.78947368,4.03007519], # curvature kappa from Simulation
+ [1.02915975,1.573720805,2.407706364,2.790518802,3.173814476,4.187433094,4.511739072] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.22
+ [14.72680026, 13.64338887, 12.41305478, 11.66482931, 11.09781471, 9.435795985, 8.959564147], # input (moisture-content in %)
+ [1.20401338 , 1.72575251 , 2.28762542 , 2.64882943 , 2.92976589 , 3.73244147 , 3.93311037 ], # curvature kappa from Simulation
+ [1.058078122, 1.544624544, 2.317033799, 2.686043143, 2.967694189, 3.913528418, 4.262750825] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.34
+ [14.98380876,13.97154915,12.77309253,12.00959929,11.42001731,9.561447179,8.964704969], # input (moisture-content in %)
+ [0.87218045, 1.26315789, 1.7443609, 2.07518797, 2.28571429, 3.03759398, 3.27819549], # curvature kappa from Simulation
+ [0.789078472,1.1299263,1.738136936,2.159520896,2.370047499,3.088299431,3.18097558] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.43
+ [15.11316339,14.17997082,13.05739844,12.32309209,11.74608518,9.812372466,9.10519385 ], # input (moisture-content in %)
+ [0.63157895, 0.93233083, 1.29323308, 1.53383459, 1.71428571, 2.34586466,2.55639098], # curvature kappa from Simulation
+ [0.577989364,0.829007544,1.094211707,1.325332511,1.400455154,1.832325697,2.047483977] # curvature kappa from Experiment
+ ],
+
+ [ # Dataset Ratio r = 0.49
+ [15.30614414,14.49463867,13.46629742,12.78388234,12.23057715,10.21852839,9.341730605], # input (moisture-content in %)
+ [0.60150376, 0.87218045, 1.23308271, 1.5037594 , 1.71428571, 2.46616541,2.79699248], # curvature kappa from Simulation
+ [0.357615902,0.376287785,0.851008627,0.904475291,1.039744708,1.346405241,1.566568558] # curvature kappa from Experiment
+ ]
+ ]
+
+
+
+ # input = [14.72680026, 13.64338887, 12.41305478, 11.66482931, 11.09781471, 9.435795985, 8.959564147] #moisture-content (in %)
+ # kappa_sim = [1.20401338 , 1.72575251, 2.28762542 , 2.64882943 , 2.92976589 , 3.73244147 , 3.93311037 ] #kappa from Simulation
+ # kappa_exp = [1.058078122,1.544624544, 2.317033799, 2.686043143, 2.967694189, 3.913528418, 4.262750825] #kappa from Experiment
+
+ # compute difference:
+ # relative_error = (np.array(kappa_sim) - np.array(kappa_exp)) / np.array(kappa_exp)
+ # print('relative_error:', relative_error)
+
+ relative_error = (np.array(dataset[dataset_number][1]) - np.array(dataset[dataset_number][2])) / np.array(dataset[dataset_number][2])
+ print('relative_error:', relative_error)
+
+
+ # --------------- Plot Lines + Scatter -----------------------
+ # line_1 = ax.plot(np.array(dataset[dataset_number][0]), np.array(dataset[dataset_number][1]), # data
+ # # color='forestgreen', # linecolor
+ # marker='D', # each marker will be rendered as a circle
+ # markersize=5, # marker size
+ # # markerfacecolor='darkorange', # marker facecolor
+ # markeredgecolor='black', # marker edgecolor
+ # markeredgewidth=0.75, # marker edge width
+ # # linestyle='dashdot', # line style will be dash line
+ # linewidth=1.5, # line width
+ # zorder=3,
+ # label = r"$\kappa_{sim}$")
+
+ # line_2 = ax.plot(np.array(dataset[dataset_number][0]), np.array(dataset[dataset_number][2]), # data
+ # # color='orangered', # linecolor
+ # marker='o', # each marker will be rendered as a circle
+ # markersize=5, # marker size
+ # # markerfacecolor='cornflowerblue', # marker facecolor
+ # markeredgecolor='black', # marker edgecolor
+ # markeredgewidth=0.75, # marker edge width
+ # # linestyle='--', # line style will be dash line
+ # linewidth=1.5, # line width
+ # zorder=3,
+ # alpha=0.8, # Change opacity
+ # label = r"$\kappa_{exp}$")
+
+
+ line_3 = ax.plot(np.array(dataset[dataset_number][0]), relative_error, # data
+ # color='orangered', # linecolor
+ marker='o', # each marker will be rendered as a circle
+ markersize=5, # marker size
+ # markerfacecolor='cornflowerblue', # marker facecolor
+ markeredgecolor='black', # marker edgecolor
+ markeredgewidth=0.75, # marker edge width
+ # linestyle='--', # line style will be dash line
+ linewidth=1.5, # line width
+ zorder=3,
+ alpha=0.8, # Change opacity
+ label = r"relative error")
+
+ # --- Plot order line
+ # x = np.linspace(0.01,1/2,100)
+ # y = CC_L2[0]*x**2
+ # OrderLine = ax.plot(x,y,linestyle='--', label=r"$\mathcal{O}(h)$")
+
+
+
+ # Fix_value = 7.674124
+ # l3 = plt.axhline(y = Fix_value, color = 'black', linewidth=0.75, linestyle = 'dashed')
+ # --------------- Set Axes -----------------------
+ # ax.set_title(r"ratio $r = 0.22$") # Plot - Title
+
+ # Plot - Title
+ match dataset_number:
+ case 0:
+ ax.set_title(r"ratio $r = 0.12$")
+ case 1:
+ ax.set_title(r"ratio $r = 0.17$")
+ case 2:
+ ax.set_title(r"ratio $r = 0.22$")
+ case 3:
+ ax.set_title(r"ratio $r = 0.34$")
+ case 4:
+ ax.set_title(r"ratio $r = 0.43$")
+ case 5:
+ ax.set_title(r"ratio $r = 0.49$")
+
+ # plt.xscale('log') # Use Logarithmic-Scale
+ # plt.yscale('log')
+ ax.set_xlabel(r"Wood moisture content $\omega (\%)$", labelpad=4)
+ ax.set_ylabel(r"relative error $\frac{\kappa_{sim}-\kappa_{exp}}{\kappa_{\exp}}$", labelpad=4)
+ plt.tight_layout()
+
+ # # --- Set Line labels
+ # line_labels = [r"$CC_{L_2}$",r"$CC_{H_1}$", r"$\mathcal{O}(h)$"]
+
+ # --- Set Legend
+ legend = ax.legend()
+ # legend = fig.legend([line_1 , line_2, OrderLine],
+ # labels = line_labels,
+ # bbox_to_anchor=[0.97, 0.50],
+ # # bbox_to_anchor=[0.97, 0.53],
+ # # loc='center',
+ # ncol=1, # Number of columns used for legend
+ # # borderaxespad=0.15, # Small spacing around legend box
+ # frameon=True,
+ # prop={'size': 10})
+
+
+ frame = legend.get_frame()
+ frame.set_edgecolor('black')
+
+
+ # --- Adjust left/right spacing:
+ # plt.subplots_adjust(right=0.81)
+ # plt.subplots_adjust(left=0.11)
+
+ # ---------- Output Figure as pdf:
+ fig.set_size_inches(width, height)
+ fig.savefig('WoodBilayer_expComparison_' + str(dataset_number) + 'relError.pdf')
+ # plt.show()
+
+
diff --git a/Plot-Scripts/WoodBilayer_expComparison_r022.py b/Plot-Scripts/WoodBilayer_expComparison_r022.py
deleted file mode 100644
index f09281bf..00000000
--- a/Plot-Scripts/WoodBilayer_expComparison_r022.py
+++ /dev/null
@@ -1,134 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import math
-import os
-import subprocess
-import fileinput
-import re
-import sys
-import matplotlib as mpl
-from mpl_toolkits.mplot3d import Axes3D
-import matplotlib.cm as cm
-import matplotlib.ticker as ticker
-from matplotlib.ticker import MultipleLocator,FormatStrFormatter,MaxNLocator
-import seaborn as sns
-import matplotlib.colors as mcolors
-# ----------------------------------------------------
-# --- Define Plot style:
-# plt.style.use("seaborn-white")
-# plt.style.use("seaborn-pastel")
-# plt.style.use("seaborn-colorblind")
-plt.style.use("seaborn")
-mpl.rcParams['text.usetex'] = True
-mpl.rcParams["font.family"] = "serif"
-mpl.rcParams["font.size"] = "14"
-mpl.rcParams['xtick.bottom'] = True
-mpl.rcParams['xtick.major.size'] = 3
-mpl.rcParams['xtick.minor.size'] = 1.5
-mpl.rcParams['xtick.major.width'] = 0.75
-mpl.rcParams['ytick.left'] = True
-mpl.rcParams['ytick.major.size'] = 3
-mpl.rcParams['ytick.minor.size'] = 1.5
-mpl.rcParams['ytick.major.width'] = 0.75
-
-#Adjust grid:
-mpl.rcParams.update({"axes.grid" : True}) # Add grid
-mpl.rcParams['axes.labelpad'] = 3
-mpl.rcParams['grid.linewidth'] = 0.25
-mpl.rcParams['grid.alpha'] = 0.9 # 0.75
-mpl.rcParams['grid.linestyle'] = '-'
-mpl.rcParams['grid.color'] = 'gray'#'black'
-mpl.rcParams['text.latex.preamble'] = r'\usepackage{amsfonts}' # Makes Use of \mathbb possible.
-# ----------------------------------------------------------------------------------------
-width = 5.79
-height = width / 1.618 # The golden ratio.
-
-# fig = plt.figure() #main
-fig, ax = plt.subplots(figsize=(width,height))
-
-
-
-
-
-input = [14.72680026, 13.64338887, 12.41305478, 11.66482931, 11.09781471, 9.435795985, 8.959564147] #moisture-content (in %)
-
-
-kappa_sim = [1.20401338 , 1.72575251, 2.28762542 , 2.64882943 , 2.92976589 , 3.73244147 , 3.93311037 ] #kappa from Simulation
-kappa_exp = [1.058078122,1.544624544, 2.317033799, 2.686043143, 2.967694189, 3.913528418, 4.262750825] #kappa from Experiment
-
-# compute difference:
-relative_error = (np.array(kappa_sim) - np.array(kappa_exp)) / np.array(kappa_exp)
-print('relative_error:', relative_error)
-
-
-# --------------- Plot Lines + Scatter -----------------------
-line_1 = ax.plot(input, kappa_sim, # data
- # color='forestgreen', # linecolor
- marker='D', # each marker will be rendered as a circle
- markersize=5, # marker size
- # markerfacecolor='darkorange', # marker facecolor
- markeredgecolor='black', # marker edgecolor
- markeredgewidth=0.75, # marker edge width
- # linestyle='dashdot', # line style will be dash line
- linewidth=1.5, # line width
- zorder=3,
- label = r"$\kappa_{sim}$")
-
-line_2 = ax.plot(input, kappa_exp, # data
- # color='orangered', # linecolor
- marker='o', # each marker will be rendered as a circle
- markersize=5, # marker size
- # markerfacecolor='cornflowerblue', # marker facecolor
- markeredgecolor='black', # marker edgecolor
- markeredgewidth=0.75, # marker edge width
- # linestyle='--', # line style will be dash line
- linewidth=1.5, # line width
- zorder=3,
- alpha=0.8, # Change opacity
- label = r"$\kappa_{exp}$")
-
-# --- Plot order line
-# x = np.linspace(0.01,1/2,100)
-# y = CC_L2[0]*x**2
-# OrderLine = ax.plot(x,y,linestyle='--', label=r"$\mathcal{O}(h)$")
-
-
-
-# Fix_value = 7.674124
-# l3 = plt.axhline(y = Fix_value, color = 'black', linewidth=0.75, linestyle = 'dashed')
-# --------------- Set Axes -----------------------
-ax.set_title(r"ratio $r = 0.22$") # Plot - Title
-# plt.xscale('log') # Use Logarithmic-Scale
-# plt.yscale('log')
-ax.set_xlabel(r"Wood moisture content $\omega (\%)$", labelpad=4)
-ax.set_ylabel(r"Curvature $\kappa$", labelpad=4)
-plt.tight_layout()
-
-# # --- Set Line labels
-# line_labels = [r"$CC_{L_2}$",r"$CC_{H_1}$", r"$\mathcal{O}(h)$"]
-
-# --- Set Legend
-legend = ax.legend()
-# legend = fig.legend([line_1 , line_2, OrderLine],
-# labels = line_labels,
-# bbox_to_anchor=[0.97, 0.50],
-# # bbox_to_anchor=[0.97, 0.53],
-# # loc='center',
-# ncol=1, # Number of columns used for legend
-# # borderaxespad=0.15, # Small spacing around legend box
-# frameon=True,
-# prop={'size': 10})
-
-
-frame = legend.get_frame()
-frame.set_edgecolor('black')
-
-
-# --- Adjust left/right spacing:
-# plt.subplots_adjust(right=0.81)
-# plt.subplots_adjust(left=0.11)
-
-# ---------- Output Figure as pdf:
-fig.set_size_inches(width, height)
-fig.savefig('WoodBilayer_expComparison.pdf')
-# plt.show()
\ No newline at end of file
diff --git a/Plot-Scripts/WoodBilayer_expComparison_r049.py b/Plot-Scripts/WoodBilayer_expComparison_r049.py
deleted file mode 100644
index 818b705a..00000000
--- a/Plot-Scripts/WoodBilayer_expComparison_r049.py
+++ /dev/null
@@ -1,136 +0,0 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import math
-import os
-import subprocess
-import fileinput
-import re
-import sys
-import matplotlib as mpl
-from mpl_toolkits.mplot3d import Axes3D
-import matplotlib.cm as cm
-import matplotlib.ticker as ticker
-from matplotlib.ticker import MultipleLocator,FormatStrFormatter,MaxNLocator
-import seaborn as sns
-import matplotlib.colors as mcolors
-# ----------------------------------------------------
-# --- Define Plot style:
-# plt.style.use("seaborn-white")
-# plt.style.use("seaborn-pastel")
-# plt.style.use("seaborn-colorblind")
-plt.style.use("seaborn")
-mpl.rcParams['text.usetex'] = True
-mpl.rcParams["font.family"] = "serif"
-mpl.rcParams["font.size"] = "14"
-mpl.rcParams['xtick.bottom'] = True
-mpl.rcParams['xtick.major.size'] = 3
-mpl.rcParams['xtick.minor.size'] = 1.5
-mpl.rcParams['xtick.major.width'] = 0.75
-mpl.rcParams['ytick.left'] = True
-mpl.rcParams['ytick.major.size'] = 3
-mpl.rcParams['ytick.minor.size'] = 1.5
-mpl.rcParams['ytick.major.width'] = 0.75
-
-#Adjust grid:
-mpl.rcParams.update({"axes.grid" : True}) # Add grid
-mpl.rcParams['axes.labelpad'] = 3
-mpl.rcParams['grid.linewidth'] = 0.25
-mpl.rcParams['grid.alpha'] = 0.9 # 0.75
-mpl.rcParams['grid.linestyle'] = '-'
-mpl.rcParams['grid.color'] = 'gray'#'black'
-mpl.rcParams['text.latex.preamble'] = r'\usepackage{amsfonts}' # Makes Use of \mathbb possible.
-# ----------------------------------------------------------------------------------------
-width = 5.79
-height = width / 1.618 # The golden ratio.
-
-# fig = plt.figure() #main
-fig, ax = plt.subplots(figsize=(width,height))
-
-
-
-
-
-input = [15.30614414,14.49463867,13.46629742,12.78388234,12.23057715,10.21852839,9.341730605] #moisture-content (in %)
-
-
-kappa_sim = [0.59118236, 0.87174349, 1.24248497, 1.49298597, 1.70340681, 2.46492986, 2.79559118] #kappa from Simulation
-kappa_exp = [0.357615902, 0.376287785, 0.851008627, 0.904475291, 1.039744708, 1.346405241, 1.566568558] #kappa from Experiment
-
-
-
-# compute difference:
-relative_error = (np.array(kappa_sim) - np.array(kappa_exp)) / np.array(kappa_exp)
-print('relative_error:', relative_error)
-
-
-# --------------- Plot Lines + Scatter -----------------------
-line_1 = ax.plot(input, kappa_sim, # data
- # color='forestgreen', # linecolor
- marker='D', # each marker will be rendered as a circle
- markersize=5, # marker size
- # markerfacecolor='darkorange', # marker facecolor
- markeredgecolor='black', # marker edgecolor
- markeredgewidth=0.75, # marker edge width
- # linestyle='dashdot', # line style will be dash line
- linewidth=1.5, # line width
- zorder=3,
- label = r"$\kappa_{sim}$")
-
-line_2 = ax.plot(input, kappa_exp, # data
- # color='orangered', # linecolor
- marker='o', # each marker will be rendered as a circle
- markersize=5, # marker size
- # markerfacecolor='cornflowerblue', # marker facecolor
- markeredgecolor='black', # marker edgecolor
- markeredgewidth=0.75, # marker edge width
- # linestyle='--', # line style will be dash line
- linewidth=1.5, # line width
- zorder=3,
- alpha=0.8, # Change opacity
- label = r"$\kappa_{exp}$")
-
-# --- Plot order line
-# x = np.linspace(0.01,1/2,100)
-# y = CC_L2[0]*x**2
-# OrderLine = ax.plot(x,y,linestyle='--', label=r"$\mathcal{O}(h)$")
-
-
-
-# Fix_value = 7.674124
-# l3 = plt.axhline(y = Fix_value, color = 'black', linewidth=0.75, linestyle = 'dashed')
-# --------------- Set Axes -----------------------
-ax.set_title(r"ratio $r = 0.49$") # Plot - Title
-# plt.xscale('log') # Use Logarithmic-Scale
-# plt.yscale('log')
-ax.set_xlabel(r"Wood moisture content $\omega (\%)$", labelpad=4)
-ax.set_ylabel(r"Curvature $\kappa$", labelpad=4)
-plt.tight_layout()
-
-# # --- Set Line labels
-# line_labels = [r"$CC_{L_2}$",r"$CC_{H_1}$", r"$\mathcal{O}(h)$"]
-
-# --- Set Legend
-legend = ax.legend()
-# legend = fig.legend([line_1 , line_2, OrderLine],
-# labels = line_labels,
-# bbox_to_anchor=[0.97, 0.50],
-# # bbox_to_anchor=[0.97, 0.53],
-# # loc='center',
-# ncol=1, # Number of columns used for legend
-# # borderaxespad=0.15, # Small spacing around legend box
-# frameon=True,
-# prop={'size': 10})
-
-
-frame = legend.get_frame()
-frame.set_edgecolor('black')
-
-
-# --- Adjust left/right spacing:
-# plt.subplots_adjust(right=0.81)
-# plt.subplots_adjust(left=0.11)
-
-# ---------- Output Figure as pdf:
-fig.set_size_inches(width, height)
-fig.savefig('WoodBilayer_expComparison_r049.pdf')
-# plt.show()
\ No newline at end of file
--
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