From 6550c2a2bd5f9c7090920bf381ec17559fe615a7 Mon Sep 17 00:00:00 2001
From: Jan Gruis <gruis@cst.rwth-aachen.de>
Date: Tue, 19 Sep 2023 15:46:03 +0200
Subject: [PATCH] added evaluation module which is currently only a copy of
tlmlib.py
---
.gitignore | 2 +
evaluation.py | 320 ++++++++++++++++++++++++++++++++++++++++++++++++++
2 files changed, 322 insertions(+)
create mode 100644 evaluation.py
diff --git a/.gitignore b/.gitignore
index 3dff88e..63831f4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,5 @@
.idea/
.ipynb_checkpoints/
__pycache__/
+
+jan_tests.ipynb
diff --git a/evaluation.py b/evaluation.py
new file mode 100644
index 0000000..71ab0f3
--- /dev/null
+++ b/evaluation.py
@@ -0,0 +1,320 @@
+"""Contains all evaluation classes and functions.
+
+class LinearRegressionFitting: Represents a Linear Regression Fitting of a 2D x,y plot
+"""
+
+import os
+import pandas as pd
+import numpy as np
+import altair as alt
+import datetime
+
+
+
+# Colors
+rwthcolors= {'blau' : '#00549F',
+ 'schwarz' : '#000000',
+ 'magenta' : '#E30066',
+ 'gelb' : '#FFED00',
+ 'petrol' : '#006165',
+ 'türkis' : '#0098A1',
+ 'grün' : '#57AB27',
+ 'maigrün' : '#BDCD00',
+ 'orange' : '#F6A800',
+ 'rot' : '#CC071E',
+ 'bordeaux' : '#A11035',
+ 'violett' : '#612158',
+ 'lila' : '#7A6FAC',
+ }
+colorlist = [rwthcolors['blau'],
+ rwthcolors['türkis'],
+ rwthcolors['grün'],
+ rwthcolors['orange'],
+ rwthcolors['violett'],
+ ]
+
+
+
+
+
+
+# Class Definitions
+class LinearRegressionFitting:
+ """Represents a Linear Regression Fitting of a 2D x,y plot"""
+
+ def __init__(self, PandaDataFrame):
+ """Initializes the instance with a PandaDataFrame
+
+ Attributes:
+ x
+ y
+ slope
+ intercept
+ residuals
+ diagnostics
+ model
+ R2
+ df
+ chart
+ """
+ self.input = PandaDataFrame
+ self.x = PandaDataFrame.to_numpy()[:, 0]
+ self.y = PandaDataFrame.to_numpy()[:, 1]
+
+ # https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html
+ [self.slope, self.intercept], self.residuals, *self.diagnostics = np.polyfit(self.x, self.y, 1, full = True)
+
+ # model = expression of polynom from polyfit, here basicly
+ # y = model(x) <=> same as writing y = slope * x + intercept
+ self.model = np.poly1d([self.slope, self.intercept])
+
+ # Bestimmtheitsmaß / Coefficient of determination / R²
+ # 1 - ( residual sum of squares / SUM( (yi - y_mean)² ) )
+ self.R2 = 1 - (self.residuals[0] /
+ np.sum( pow( self.y - np.average( self.y ), 2) )
+ )
+
+ self.df = pd.DataFrame({'x': self.x,'f(x)': self.model(self.x)})
+ self.chart = alt.Chart(self.df).mark_line().encode(x="x", y='f(x)')
+
+class RT_Evaluation(LinearRegressionFitting):
+ def __init__(self, PandaDataFrame, contactlenght):
+ super().__init__(PandaDataFrame)
+ #self.input
+ #self.x
+ #self.y
+ #self.slope
+ #self.intercept
+ #self.residuals
+ #self.diagnostics
+ #self.model
+ #self.R2
+ #self.df
+ model_df_x = np.linspace(0, self.x[-1])
+ self.df = pd.DataFrame({'x': model_df_x,'f(x)': self.model(model_df_x)})
+ #self.chart
+ self.chart = alt.Chart(self.df).mark_line().encode(x="x", y='f(x)')
+
+ self.contactlenght = contactlenght
+
+ # Kontaktwiderstand Rc [Ohm*mm]
+ # = RT(d=0)/2 [Ohm] * Contactlenght [µm/1000]
+ self.Rc = (self.intercept/2) * (contactlenght/1000)
+
+ # Schichtwiderstand [Ohm/sq = Ohm]
+ # = slope [Ohm/µm] * Contactlenght [µm]
+ self.Rsh = self.slope * contactlenght
+
+ # Transferlänge LT [mm] RT(d) = 0
+ # = slope [Ohm/µm] / RT(d=0) [Ohm] / 1000
+ #self.LT = self.intercept / self.slope / 1000
+
+ # Transferlänge LT [µm] RT(d) = 0
+ # = slope [Ohm/µm] / RT(d=0) [Ohm]
+ self.LT = self.intercept / self.slope / 2
+
+ # LT = sqrt(rhoc/Rsh).
+ # "Semiconductor Material and Device Characterization Third Edition",
+ # D. Schroder, p. 140, Eq. 3.21
+ # Spezifischer Kontaktwiderstand rhoc = LT² * Rsh
+ # = Ohm cm² | µm²*0.00000001 = cm²
+ self.rhoc = self.LT*self.LT * self.Rsh * 1E-4 * 1E-4
+
+
+class TlmMeasurement(object):
+ def __init__(self, filelist, distances = (5, 10, 15, 20, 50), contactlenght = 50):
+ """filelist as tuple
+ distances as tuple ## Abstände der TLM in µm
+ contactlenght # Kontaktweite der TLM Strukturen in µm
+ """
+ self._creation_date = datetime.datetime.now()
+ self.filelist = filelist
+ self.path, self.files = self.importfiles(filelist)
+ self.distances = distances
+ self.contactlenght = contactlenght
+ self.df = self.construct_dataframes(self.filelist)
+ #self.df_org = self.df
+ self.R, self.lin_reg_charts, self.R_statistics = self.R_from_lin_reg()
+ self.RT0 = pd.DataFrame({'d/µm':self.distances, 'R_T/Ohm':self.R})
+
+ self.eval0 = RT_Evaluation(self.RT0, self.contactlenght)
+ self.eval1, self.eval2 = self.find_RT1_RT2()
+ self.refined = False
+ #self.results = self.results()
+
+ def importfiles(self, filelist):
+ if not len(filelist) == 5:
+ raise Exception("Files Missing - I need 5 files for TLM or CTLM")
+ filenames = []
+ name = ""
+ measurement = []
+ for i in range(len(filelist)):
+ filenames.append(os.path.split(filelist[i])[1])
+ name, end = filenames[i].rsplit(sep="_",maxsplit=1)
+ #print(name, "_", end)
+ if i == 0:
+ firstname = name
+ elif not name == firstname:
+ print("Files:", filenames)
+ raise Exception("Filenames differ")
+ measurement.append(end.split(sep=".")[0])
+ #print(measurement)
+ #print(i, len(files)-1)
+ if (i == len(filelist)-1) and (not measurement == ['1', '2', '3', '4', '5']):
+ print("Files:", filenames)
+ raise Exception("Not Measurement _1 to _5?")
+ path = os.path.split(filelist[0])[0]
+ return (path, filenames)
+
+ def construct_dataframes(self, filelist = None):
+ if filelist is None:
+ filelist = self.filelist
+ df = []
+ for i in range(len(filelist)):
+ df.append(pd.read_csv(filelist[i], sep=" ", skip_blank_lines=True, header=3, index_col=0, usecols=[0,1,2,3], names=["#","U/V","I/A","R/Ohm"]))
+ return df
+
+ def construct_ui_charts(self, PandaDataFrames = None):
+ if PandaDataFrames is None:
+ PandaDataFrames = self.df
+ uicharts = []
+ for i in range(len(PandaDataFrames)):
+ uicharts.append( alt.Chart(self.df[i]).mark_point(color=colorlist[i]).encode(x="U/V", y="I/A").interactive() )
+ return uicharts
+
+ def R_from_lin_reg(self, PandaDataFrames = None):
+ if PandaDataFrames is None:
+ PandaDataFrames = self.df
+ lin_reg_results = [] # R
+ lin_reg_charts = []
+ statistics_string = f"Statistics for Fitting of R Values \n d / µm | R² | RT / Ohm"
+ #print(f"Statistics for Fitting of R Values \n {'d / µm' : >9} | {'R²' : <6} | RT / Ohm ")
+ for i in range(len(PandaDataFrames)):
+ fit = LinearRegressionFitting(PandaDataFrames[i])
+ lin_reg_results.append( 1 / (fit.slope) )
+ lin_reg_charts.append( alt.Chart(fit.df).mark_line(color=colorlist[i]).encode(x="x", y='f(x)') )
+ #print(f"{self.distances[i] : 10d} | {fit.R2 : >5.4f} | {lin_reg_results[i] : 10.4f}")
+ statistics_string += "\n" + f"{self.distances[i] : 10d} | {fit.R2 : >5.4f} | {lin_reg_results[i] : 10.4f}"
+ #print(statistics_string)
+ return lin_reg_results, lin_reg_charts, statistics_string
+
+ def contruct_lin_reg_fit_charts(self):
+ return self.lin_reg_charts
+
+ def find_RT1_RT2(self, R = None):
+ if R is None:
+ R = self.RT0
+
+ # Remove One Measurement
+ # Find maximal Bestimmtheitsmaß / Coefficient of determination / R²
+ save_RT1 = None
+ max = 0
+ for i in range(len(R)):
+ Ri = R.drop(labels=i, axis=0)
+ evali = RT_Evaluation(Ri, self.contactlenght)
+ if evali.R2 >= max:
+ max = evali.R2
+ save_RT1 = evali
+
+ # Remove Two Measurements
+ list_of_rows_to_remove = [[0,1],
+ [0,2],
+ [0,3],
+ [0,4],
+ [1,2],
+ [1,3],
+ [1,4],
+ [2,3],
+ [2,4],
+ [3,4],
+ ]
+ save_RT2 = None
+ max = 0
+ for rows in list_of_rows_to_remove:
+ Ri = R.drop(labels=rows, axis=0)
+ evali = RT_Evaluation(Ri, self.contactlenght)
+ if evali.R2 >= max:
+ max = evali.R2
+ save_RT2 = evali
+
+ return save_RT1, save_RT2
+
+ def refine_evaluated_range(self, newrange = None):
+ if not type(newrange) is tuple:
+ raise Exception("Wrong format for Newrange! Should be tuple (float, float)")
+ if not len(newrange) == 2:
+ raise Exception("Two few or many values in newrange! Should be tuple (float, float)")
+ minI, maxI = newrange
+ refined_df = self.df
+ for i in range(len(refined_df)):
+ # First vector where values for I/A are bigger than min and smaller than max = True
+ # (dataframe.iloc[:,1] > min) & (dataframe.iloc[:,1] < max )
+ # then this boolean vector is used as indexer for the dame dataframe
+ # every value with False is sliced
+ work = refined_df[i]
+ #print(work)
+ #print((dataframe.iloc[:,1] > minI) & (dataframe.iloc[:,1] < maxI ))
+ work = work [ (work.iloc[:,1] > minI) & (work.iloc[:,1] < maxI ) ]
+ #print(work)
+ refined_df[i] = work
+ self.df = refined_df
+ self.R, self.lin_reg_charts, statistics_string = self.R_from_lin_reg()
+ self.RT0 = pd.DataFrame({'d/µm':self.distances, 'R_T/Ohm':self.R})
+ self.eval0 = RT_Evaluation(self.RT0, self.contactlenght)
+ self.eval1, self.eval2 = self.find_RT1_RT2()
+ self.refined = True
+
+ def results(self, comment=""):
+ #print("Path:\n", ctlm.path, "\n", sep = "")
+ results_string = "Path: " + self.path + "\n"
+ #print("Files: {}, {}, {}, {}, {} \n".format(*ctlm.files))
+ results_string += "Files: {}, {}, {}, {}, {} \n".format(*self.files)
+ #header = "Feld Rsh R² Rc LT rhoc min I max I # removed values"
+ #header = "Feld Rsh R² Rc LT rhoc # removed values"
+ results_string += comment + " \n"
+ results_string += "Feld Rsh R² Rc LT rhoc # removed values \n"
+ #units = "- [Ohm/sq] - [Ohm mm] [µm] [Ohm cm²] [A] [A] -"
+ #units = "- [Ohm/sq] - [Ohm mm] [µm] [Ohm cm²] -"
+ results_string += "- [Ohm/sq] - [Ohm mm] [µm] [Ohm cm²] - \n"
+ format_string = "{:<4}{:7.2f} {:8.4f} {:6.2f} {:8.2f} {:8.2e} {} \n"
+ results_string += format_string.format(self.files[0].rsplit("_")[0],
+ self.eval0.Rsh,
+ self.eval0.R2,
+ self.eval0.Rc,
+ self.eval0.LT,
+ self.eval0.rhoc,
+ #minI,
+ #maxI,
+ None,
+ )
+ results_string += format_string.format(self.files[0].rsplit("_")[0],
+ self.eval1.Rsh,
+ self.eval1.R2,
+ self.eval1.Rc,
+ self.eval1.LT,
+ self.eval1.rhoc,
+ #minI,
+ #maxI,
+ 1,
+ )
+ results_string += format_string.format(self.files[0].rsplit("_")[0],
+ self.eval2.Rsh,
+ self.eval2.R2,
+ self.eval2.Rc,
+ self.eval2.LT,
+ self.eval2.rhoc,
+ #minI,
+ #maxI,
+ 2,
+ )
+
+ return results_string
+
+ def construct_rt_charts(self):
+ rt_charts = []
+ rt_charts.append( alt.Chart(self.RT0).mark_point(color=rwthcolors['blau']).encode(x='d/µm', y='R_T/Ohm').interactive() )
+ rt_charts.append( alt.Chart(self.eval0.df).mark_line(color=rwthcolors['bordeaux']).encode(x='x', y='f(x)') )
+ rt_charts.append( alt.Chart(self.eval1.df).mark_line(color=rwthcolors['violett']).encode(x='x', y='f(x)') )
+ rt_charts.append( alt.Chart(self.eval2.df).mark_line(color=rwthcolors['lila']).encode(x='x', y='f(x)') )
+ return rt_charts
+
--
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