diff --git a/CTLM.ipynb b/CTLM.ipynb
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-{
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- "# CTLM - konstanter Innenradius!\n",
- "1.1 - 2022-09-05\n",
- "\n",
- "Jan Gruis \\ Thorsten Zweipfennig \\\n",
- "Compound Semiconductor Technology \\\n",
- "RWTH Aachen University"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "289db4ef",
- "metadata": {},
- "source": [
- "## Usage\n",
- "* _Run_ the first cell with code by selcting it with a click and pressing **SHIFT + ENTER or clicking on \"▶ Run\"**\n",
- "* click \"File select\" which appeared under the cell and select the five files from one CTLM measurement\n",
- "* _Run_ the next cell\n",
- "* Consider the results below the cell\n",
- "* Choose min and max current values (default is minI = -0.03 and maxI = 0.03) and type them at the top of the next cell, than run that cell"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a2bdc98b",
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- "source": [
- "### CTLM Physik\n",
- "\"Semiconductor Material and Device Characterization Third Edition\", D. Schroder, p. 140, Eq. 3.21 \\\n",
- "\"Semiconductor Material and Device Characterization Third Edition\", D. Schroder, p. 144, Eq. 3.31\n",
- "\n",
- "### Mathe\n",
- "https://de.wikipedia.org/wiki/Bestimmtheitsma%C3%9F \\\n",
- "https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html# \n",
- "\n",
- "### Einstieg\n",
- "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_numpy.html \\\n",
- "https://numpy.org/devdocs/user/absolute_beginners.html \\\n",
- "https://edusecrets.com/lesson-02-creating-a-file-select-button-in-jupyter-notebook/\n",
- "\n",
- "### Zeichnen\n",
- "https://www.practicaldatascience.org/html/plotting_altair_part1.html \\\n",
- "https://altair-viz.github.io/user_guide/data.html "
- ]
- },
- {
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- "execution_count": 1,
- "id": "3ce06363",
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- "Button(description='File select', style=ButtonStyle())"
- ]
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- "source": [
- "# CTLM Parameter\n",
- "distance = (5, 10, 15, 20, 50) # Abstände der CTLM Kreise in µm\n",
- "innen = 55 # Innenradien der CTLM Strukturen in µm (55)\n",
- "\n",
- "\n",
- "# File selection\n",
- "from ipywidgets import Button\n",
- "from IPython.display import clear_output, display\n",
- "from tkinter import Tk, filedialog\n",
- "\n",
- "def select_files(b):\n",
- " #clear_output() # Button is deleted after it is clicked.\n",
- " root = Tk()\n",
- " root.withdraw() # Hide the main window.\n",
- " root.call('wm', 'attributes', '.', '-topmost', True) # Raise the root to the top of all windows.\n",
- " b.files = filedialog.askopenfilename(multiple=True) # List of selected files will be set button's file attribute. \n",
- " #print(b.files) \n",
- " \n",
- "fileselect = Button(description=\"File select\")\n",
- "fileselect.on_click(select_files)\n",
- "\n",
- "display(fileselect)"
- ]
- },
- {
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- "text": [
- "Statistics for Fitting of R Values \n",
- " d / µm | R² | RT / Ohm | C-Values\n",
- " 5 | 1.0000 | 7.4701 | 0.9571\n",
- " 10 | 1.0000 | 11.7377 | 0.9188\n",
- " 15 | 1.0000 | 16.0671 | 0.8843\n",
- " 20 | 1.0000 | 20.6316 | 0.8529\n",
- " 50 | 0.9999 | 47.2309 | 0.7113\n"
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- "C:/Users/zweipfennig/Documents/13 - local workspace/Proben/Aixtron_200_mm/PB00602R_6/PB00602R_6_E5 (22001)/Electrical/mesa - TLM/CTLM 4Contact\n",
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- "Files: M14_CTLM_4Kontakt_1.txt, M14_CTLM_4Kontakt_2.txt, M14_CTLM_4Kontakt_3.txt, M14_CTLM_4Kontakt_4.txt, M14_CTLM_4Kontakt_5.txt \n",
- "\n",
- "Feld Rsh R² Rc LT rhoc # removed values\n",
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- "M14 306.09 1.0000 0.50 1.64 8.28e-06 None\n",
- "M14 306.95 1.0000 0.49 1.59 7.74e-06 1\n",
- "M14 306.58 1.0000 0.50 1.62 8.05e-06 2\n"
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- " \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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- " maybeLoadScript(\"vega\", \"5\")\n",
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- ],
- "text/plain": [
- "alt.LayerChart(...)"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# Imports\n",
- "import os\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import altair as alt\n",
- "alt.renderers.enable('default')\n",
- "#alt.renderers.enable('notebook')\n",
- "\n",
- "# Colors\n",
- "rwthcolors= {'blau' : '#00549F',\n",
- " 'schwarz' : '#000000',\n",
- " 'magenta' : '#E30066',\n",
- " 'gelb' : '#FFED00',\n",
- " 'petrol' : '#006165',\n",
- " 'türkis' : '#0098A1',\n",
- " 'grün' : '#57AB27',\n",
- " 'maigrün' : '#BDCD00',\n",
- " 'orange' : '#F6A800',\n",
- " 'rot' : '#CC071E',\n",
- " 'bordeaux' : '#A11035',\n",
- " 'violett' : '#612158',\n",
- " 'lila' : '#7A6FAC',\n",
- " }\n",
- "colorlist = [rwthcolors['blau'],\n",
- " rwthcolors['türkis'],\n",
- " rwthcolors['grün'],\n",
- " rwthcolors['orange'],\n",
- " rwthcolors['violett'],\n",
- " ]\n",
- "\n",
- "\n",
- "# Class Definitions\n",
- "class LinearRegressionFitting:\n",
- " def __init__(self, PandaDataFrame):\n",
- " self.input = PandaDataFrame\n",
- " self.x = PandaDataFrame.to_numpy()[:, 0]\n",
- " self.y = PandaDataFrame.to_numpy()[:, 1]\n",
- "\n",
- " # https://numpy.org/doc/stable/reference/generated/numpy.polyfit.html\n",
- " [self.slope, self.intercept], self.residuals, *self.diagnostics = np.polyfit(self.x, self.y, 1, full = True)\n",
- " \n",
- " # model = expression of polynom from polyfit, here basicly\n",
- " # y = model(x) <=> same as writing y = slope * x + intercept\n",
- " self.model = np.poly1d([self.slope, self.intercept])\n",
- " \n",
- " # Bestimmtheitsmaß / Coefficient of determination / R²\n",
- " # 1 - ( residual sum of squares / SUM( (yi - y_mean)² ) )\n",
- " self.R2 = 1 - (self.residuals[0] /\n",
- " np.sum( pow( self.y - np.average( self.y ), 2) )\n",
- " )\n",
- " \n",
- " self.df = pd.DataFrame({'x': self.x,'f(x)': self.model(self.x)})\n",
- " self.chart = alt.Chart(self.df).mark_line().encode(x=\"x\", y='f(x)')\n",
- "\n",
- "#testx = np.linspace(1,100, num = 101)\n",
- "#TEST = pd.DataFrame({'x': testx,'f(x)': pow(testx,2)} )\n",
- "#TEST = pd.DataFrame({'x': testx,'f(x)': np.sin(testx) } )\n",
- "#fitting = LinearRegressionFitting(TEST).chart\n",
- "#display(alt.Chart(TEST).mark_point().encode(x=\"x\", y='f(x)') + fitting)\n",
- "\n",
- "class RT_Evaluation(LinearRegressionFitting):\n",
- " def __init__(self, PandaDataFrame, contactlenght):\n",
- " super().__init__(PandaDataFrame) \n",
- " #self.input\n",
- " #self.x\n",
- " #self.y\n",
- " #self.slope\n",
- " #self.intercept\n",
- " #self.residuals\n",
- " #self.diagnostics\n",
- " #self.model\n",
- " #self.R2\n",
- " #self.df\n",
- " model_df_x = np.linspace(0, self.x[-1])\n",
- " self.df = pd.DataFrame({'x': model_df_x,'f(x)': self.model(model_df_x)})\n",
- " #self.chart\n",
- " self.chart = alt.Chart(self.df).mark_line().encode(x=\"x\", y='f(x)')\n",
- " \n",
- " self.contactlenght = contactlenght\n",
- "\n",
- " # Kontaktwiderstand Rc [Ohm*mm]\n",
- " # = RT(d=0)/2 [Ohm] * Contactlenght [µm/1000] \n",
- " self.Rc = (self.intercept/2) * (contactlenght/1000)\n",
- "\n",
- " # Schichtwiderstand [Ohm/sq = Ohm]\n",
- " # = slope [Ohm/µm] * Contactlenght [µm]\n",
- " self.Rsh = self.slope * contactlenght\n",
- "\n",
- " # Transferlänge LT [mm] RT(d) = 0\n",
- " # = slope [Ohm/µm] / RT(d=0) [Ohm] / 1000\n",
- " #self.LT = self.intercept / self.slope / 1000\n",
- "\n",
- " # Transferlänge LT [µm] RT(d) = 0\n",
- " # = slope [Ohm/µm] / RT(d=0) [Ohm]\n",
- " self.LT = self.intercept / self.slope / 2\n",
- " \n",
- " # LT = sqrt(rhoc/Rsh).\n",
- " # \"Semiconductor Material and Device Characterization Third Edition\",\n",
- " # D. Schroder, p. 140, Eq. 3.21 \n",
- " # Spezifischer Kontaktwiderstand rhoc = LT² * Rsh\n",
- " # = Ohm cm² | µm²*0.00000001 = cm²\n",
- " self.rhoc = self.LT*self.LT * self.Rsh * 1E-4 * 1E-4\n",
- "\n",
- " \n",
- "import datetime\n",
- "class CtlmMeasurement(object):\n",
- " def __init__(self, filelist, distances = (5, 10, 15, 25, 45), inner_radius = 55):\n",
- " \"\"\"filelist as tuple\n",
- " distances as tuple ## Abstände der TLM in µm\n",
- " contactlenght # Kontaktweite der TLM Strukturen in µm \n",
- " \"\"\"\n",
- " self._creation_date = datetime.datetime.now()\n",
- " self.filelist = filelist\n",
- " self.path, self.files = self.importfiles(filelist)\n",
- " self.distances = distances\n",
- " self.inner_radius = inner_radius\n",
- " self.inner_circumference = 2*np.pi*self.inner_radius\n",
- " # \"Semiconductor Material and Device Characterization Third Edition\",\n",
- " # D. Schroder, p. 144, Eq. 3.31\n",
- " self.correction_factor = []\n",
- " for i in range(len(self.distances)):\n",
- " self.correction_factor.append((self.inner_radius / self.distances[i]) *\n",
- " np.log(1 + self.distances[i] / self.inner_radius) )\n",
- " #print(self.correction_factor)\n",
- " self.df = self.construct_dataframes(self.filelist)\n",
- " #self.df_org = self.df\n",
- " self.R, self.lin_reg_charts, self.R_statistics = self.R_from_lin_reg()\n",
- " self.RT0 = pd.DataFrame({'d/µm':self.distances, 'R_T/Ohm':self.R})\n",
- " \n",
- " self.eval0 = RT_Evaluation(self.RT0, self.inner_circumference)\n",
- " self.eval1, self.eval2 = self.find_RT1_RT2()\n",
- " self.refined = False\n",
- " \n",
- " def importfiles(self, filelist):\n",
- " if not len(filelist) == 5:\n",
- " raise Exception(\"Files Missing - I need 5 files for TLM or CTLM\")\n",
- " filenames = []\n",
- " name = \"\"\n",
- " measurement = []\n",
- " for i in range(len(filelist)):\n",
- " filenames.append(os.path.split(filelist[i])[1])\n",
- " name, end = filenames[i].rsplit(sep=\"_\",maxsplit=1)\n",
- " #print(name, \"_\", end)\n",
- " if i == 0:\n",
- " firstname = name\n",
- " elif not name == firstname:\n",
- " print(\"Files:\", filenames)\n",
- " raise Exception(\"Filenames differ\")\n",
- " measurement.append(end.split(sep=\".\")[0])\n",
- " #print(measurement)\n",
- " #print(i, len(files)-1)\n",
- " if (i == len(filelist)-1) and (not measurement == ['1', '2', '3', '4', '5']):\n",
- " print(\"Files:\", filenames)\n",
- " raise Exception(\"Not Measurement _1 to _5?\")\n",
- " path = os.path.split(filelist[0])[0]\n",
- " return (path, filenames)\n",
- " \n",
- " def construct_dataframes(self, filelist = None):\n",
- " if filelist is None:\n",
- " filelist = self.filelist\n",
- " df = []\n",
- " for i in range(len(filelist)):\n",
- " 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\"]))\n",
- " return df\n",
- " \n",
- " def construct_ui_charts(self, PandaDataFrames = None):\n",
- " if PandaDataFrames is None:\n",
- " PandaDataFrames = self.df\n",
- " uicharts = []\n",
- " for i in range(len(PandaDataFrames)):\n",
- " uicharts.append( alt.Chart(self.df[i]).mark_point(color=colorlist[i]).encode(x=\"U/V\", y=\"I/A\").interactive() )\n",
- " return uicharts\n",
- " \n",
- " def R_from_lin_reg(self, PandaDataFrames = None):\n",
- " if PandaDataFrames is None:\n",
- " PandaDataFrames = self.df\n",
- " lin_reg_results = [] # R\n",
- " lin_reg_charts = []\n",
- " statistics_string = f\"Statistics for Fitting of R Values \\n d / µm | R² | RT / Ohm | C-Values\"\n",
- " #print(f\"Statistics for Fitting of R Values \\n {'d / µm' : >9} | {'R²' : <6} | RT / Ohm \")\n",
- " for i in range(len(PandaDataFrames)):\n",
- " fit = LinearRegressionFitting(PandaDataFrames[i])\n",
- " lin_reg_results.append( 1 / (fit.slope * self.correction_factor[i]) ) \n",
- " lin_reg_charts.append( alt.Chart(fit.df).mark_line(color=colorlist[i]).encode(x=\"x\", y='f(x)') )\n",
- " #print(f\"{self.distances[i] : 10d} | {fit.R2 : >5.4f} | {lin_reg_results[i] : 10.4f}\")\n",
- " statistics_string += \"\\n\" + f\"{self.distances[i] : 10d} | {fit.R2 : >5.4f} | {lin_reg_results[i] : 10.4f} |{self.correction_factor[i] : .4f}\"\n",
- " #print(statistics_string)\n",
- " return lin_reg_results, lin_reg_charts, statistics_string\n",
- " \n",
- " def contruct_lin_reg_fit_charts(self):\n",
- " return self.lin_reg_charts \n",
- "\n",
- " def find_RT1_RT2(self, R = None):\n",
- " if R is None:\n",
- " R = self.RT0\n",
- "\n",
- " # Remove One Measurement\n",
- " # Find maximal Bestimmtheitsmaß / Coefficient of determination / R²\n",
- " save_RT1 = None\n",
- " max = 0\n",
- " for i in range(len(R)):\n",
- " Ri = R.drop(labels=i, axis=0)\n",
- " evali = RT_Evaluation(Ri, self.inner_circumference)\n",
- " if evali.R2 >= max:\n",
- " max = evali.R2\n",
- " save_RT1 = evali\n",
- "\n",
- " # Remove Two Measurements\n",
- " list_of_rows_to_remove = [[0,1],\n",
- " [0,2],\n",
- " [0,3],\n",
- " [0,4],\n",
- " [1,2],\n",
- " [1,3],\n",
- " [1,4],\n",
- " [2,3],\n",
- " [2,4],\n",
- " [3,4],\n",
- " ]\n",
- " save_RT2 = None\n",
- " max = 0\n",
- " for rows in list_of_rows_to_remove:\n",
- " Ri = R.drop(labels=rows, axis=0)\n",
- " evali = RT_Evaluation(Ri, self.inner_circumference)\n",
- " if evali.R2 >= max:\n",
- " max = evali.R2\n",
- " save_RT2 = evali\n",
- " \n",
- " return save_RT1, save_RT2\n",
- "\n",
- " def refine_evaluated_range(self, newrange = None):\n",
- " if not type(newrange) is tuple:\n",
- " raise Exception(\"Wrong format for Newrange! Should be tuple (float, float)\")\n",
- " if not len(newrange) == 2:\n",
- " raise Exception(\"Two few or many values in newrange! Should be tuple (float, float)\")\n",
- " minI, maxI = newrange\n",
- " refined_df = self.df\n",
- " for i in range(len(refined_df)):\n",
- " # First vector where values for I/A are bigger than min and smaller than max = True\n",
- " # (dataframe.iloc[:,1] > min) & (dataframe.iloc[:,1] < max )\n",
- " # then this boolean vector is used as indexer for the dame dataframe\n",
- " # every value with False is sliced\n",
- " work = refined_df[i]\n",
- " #print(work)\n",
- " #print((dataframe.iloc[:,1] > minI) & (dataframe.iloc[:,1] < maxI ))\n",
- " work = work [ (work.iloc[:,1] > minI) & (work.iloc[:,1] < maxI ) ]\n",
- " #print(work)\n",
- " refined_df[i] = work\n",
- " self.df = refined_df\n",
- " self.R, self.lin_reg_charts, statistics_string = self.R_from_lin_reg()\n",
- " self.RT0 = pd.DataFrame({'d/µm':self.distances, 'R_T/Ohm':self.R})\n",
- " self.eval0 = RT_Evaluation(self.RT0, self.inner_circumference)\n",
- " self.eval1, self.eval2 = self.find_RT1_RT2()\n",
- " self.refined = True\n",
- "\n",
- "\n",
- "ctlm = CtlmMeasurement(fileselect.files, distance, innen) #, contactlenght)\n",
- "\n",
- "print(ctlm.R_statistics)\n",
- "\n",
- "display(alt.layer(*ctlm.construct_ui_charts(), *ctlm.contruct_lin_reg_fit_charts()))\n",
- "\n",
- "CTLM = alt.Chart(ctlm.RT0).mark_point(color=rwthcolors['blau']).encode(x='d/µm', y='R_T/Ohm').interactive()\n",
- "CTLM_RT0_fit = alt.Chart(ctlm.eval0.df).mark_line(color=rwthcolors['bordeaux']).encode(x='x', y='f(x)')\n",
- "CTLM_RT1_fit = alt.Chart(ctlm.eval1.df).mark_line(color=rwthcolors['violett']).encode(x='x', y='f(x)')\n",
- "CTLM_RT2_fit = alt.Chart(ctlm.eval2.df).mark_line(color=rwthcolors['lila']).encode(x='x', y='f(x)')\n",
- "\n",
- "print(\"Path:\\n\", ctlm.path, \"\\n\", sep = \"\")\n",
- "print(\"Files: {}, {}, {}, {}, {} \\n\".format(*ctlm.files))\n",
- "#header = \"Feld Rsh R² Rc LT rhoc min I max I # removed values\"\n",
- "header = \"Feld Rsh R² Rc LT rhoc # removed values\"\n",
- "#units = \"- [Ohm/sq] - [Ohm mm] [µm] [Ohm cm²] [A] [A] -\"\n",
- "units = \"- [Ohm/sq] - [Ohm mm] [µm] [Ohm cm²] -\"\n",
- "format_string = \"{:<4}{:7.2f} {:8.4f} {:6.2f} {:8.2f} {:8.2e} {}\"\n",
- "line0 = format_string.format(ctlm.files[0].rsplit(\"_\")[0],\n",
- " ctlm.eval0.Rsh,\n",
- " ctlm.eval0.R2,\n",
- " ctlm.eval0.Rc,\n",
- " ctlm.eval0.LT,\n",
- " ctlm.eval0.rhoc,\n",
- " #minI,\n",
- " #maxI,\n",
- " None,\n",
- " )\n",
- "line1 = format_string.format(ctlm.files[0].rsplit(\"_\")[0],\n",
- " ctlm.eval1.Rsh,\n",
- " ctlm.eval1.R2,\n",
- " ctlm.eval1.Rc,\n",
- " ctlm.eval1.LT,\n",
- " ctlm.eval1.rhoc,\n",
- " #minI,\n",
- " #maxI,\n",
- " 1,\n",
- " )\n",
- "line2 = format_string.format(ctlm.files[0].rsplit(\"_\")[0],\n",
- " ctlm.eval2.Rsh,\n",
- " ctlm.eval2.R2,\n",
- " ctlm.eval2.Rc,\n",
- " ctlm.eval2.LT,\n",
- " ctlm.eval2.rhoc,\n",
- " #minI,\n",
- " #maxI,\n",
- " 2,\n",
- " )\n",
- "\n",
- "\n",
- "print(header)\n",
- "print(units)\n",
- "print(line0)\n",
- "print(line1)\n",
- "print(line2)\n",
- "\n",
- "display(alt.layer(CTLM, CTLM_RT0_fit, CTLM_RT1_fit, CTLM_RT2_fit))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "id": "95b33cd6",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Statistics for Fitting of R Values \n",
- " d / µm | R² | RT / Ohm | C-Values\n",
- " 5 | 1.0000 | 7.4701 | 0.9571\n",
- " 10 | 1.0000 | 11.7377 | 0.9188\n",
- " 15 | 1.0000 | 16.0671 | 0.8843\n",
- " 20 | 1.0000 | 20.6316 | 0.8529\n",
- " 50 | 0.9999 | 47.2309 | 0.7113\n"
- ]
- },
- {
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- "</script>"
- ],
- "text/plain": [
- "alt.LayerChart(...)"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "minI = -0.03\n",
- "maxI = 0.03\n",
- "cutoffvalues = (minI, maxI)\n",
- "\n",
- "ctlm.refine_evaluated_range(cutoffvalues)\n",
- "\n",
- "print(ctlm.R_statistics)\n",
- "\n",
- "display(alt.layer(*ctlm.construct_ui_charts(), *ctlm.contruct_lin_reg_fit_charts()))\n",
- "\n",
- "CTLM = alt.Chart(ctlm.RT0).mark_point(color=rwthcolors['blau']).encode(x='d/µm', y='R_T/Ohm').interactive()\n",
- "CTLM_RT0_fit = alt.Chart(ctlm.eval0.df).mark_line(color=rwthcolors['bordeaux']).encode(x='x', y='f(x)')\n",
- "CTLM_RT1_fit = alt.Chart(ctlm.eval1.df).mark_line(color=rwthcolors['violett']).encode(x='x', y='f(x)')\n",
- "CTLM_RT2_fit = alt.Chart(ctlm.eval2.df).mark_line(color=rwthcolors['lila']).encode(x='x', y='f(x)')\n",
- "\n",
- "print(\"Path:\\n\", ctlm.path, \"\\n\", sep = \"\")\n",
- "print(\"Files: {}, {}, {}, {}, {} \\n\".format(*ctlm.files))\n",
- "\n",
- "header = \"Feld Rsh R² Rc LT rhoc I_min I_max #_removed_values\"\n",
- "units = \"- [Ohm/sq] - [Ohm*mm] [µm] [Ohm*cm²] [A] [A] -\"\n",
- "format_string = \"{:<14} {:7.2f} {:8.4f} {:6.2f} {:8.2f} {:8.2e} {: 6.4f} {: 5.4f} {}\"\n",
- "line0 = format_string.format(ctlm.files[0],\n",
- " ctlm.eval0.Rsh,\n",
- " ctlm.eval0.R2,\n",
- " ctlm.eval0.Rc,\n",
- " ctlm.eval0.LT,\n",
- " ctlm.eval0.rhoc,\n",
- " minI,\n",
- " maxI,\n",
- " 0,\n",
- " )\n",
- "line1 = format_string.format(ctlm.files[0],\n",
- " ctlm.eval1.Rsh,\n",
- " ctlm.eval1.R2,\n",
- " ctlm.eval1.Rc,\n",
- " ctlm.eval1.LT,\n",
- " ctlm.eval1.rhoc,\n",
- " minI,\n",
- " maxI,\n",
- " 1,\n",
- " )\n",
- "line2 = format_string.format(ctlm.files[0],\n",
- " ctlm.eval2.Rsh,\n",
- " ctlm.eval2.R2,\n",
- " ctlm.eval2.Rc,\n",
- " ctlm.eval2.LT,\n",
- " ctlm.eval2.rhoc,\n",
- " minI,\n",
- " maxI,\n",
- " 2,\n",
- " )\n",
- "\n",
- "result_path = ctlm.path + \"/CTLM_results.txt\"\n",
- "if not os.path.exists(result_path):\n",
- " with open(result_path, 'w') as f:\n",
- " f.write(header)\n",
- " f.write(os.linesep)\n",
- " f.write(units)\n",
- " f.write(os.linesep)\n",
- "with open(result_path, \"a\") as f:\n",
- " f.write(line0)\n",
- " f.write(os.linesep)\n",
- " f.write(line1)\n",
- " f.write(os.linesep)\n",
- " f.write(line2)\n",
- " f.write(os.linesep)\n",
- "\n",
- "print(header)\n",
- "print(units)\n",
- "print(line0)\n",
- "print(line1)\n",
- "print(line2)\n",
- "\n",
- "display(alt.layer(CTLM, CTLM_RT0_fit, CTLM_RT1_fit, CTLM_RT2_fit))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "1337afe2",
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "2d915e86",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3 (ipykernel)",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.4"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}