diff --git a/ausarbeitung_laborversuch.ipynb b/ausarbeitung_laborversuch.ipynb
index b146098085b61edd22c0e9d5674a8e7a0fc58f16..755362e265c16fb605d374af093513c6e78f1254 100644
--- a/ausarbeitung_laborversuch.ipynb
+++ b/ausarbeitung_laborversuch.ipynb
@@ -1,925 +1,1048 @@
 {
-    "cells": [
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "# Kalorimetrie Laborversuch\n",
-                "\n",
-                "Author: XXXXX\n",
-                "\n",
-                "Datum: XXXXX\n",
-                "\n",
-                "Gruppe: XXXXX"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Forschungsfrage\n",
-                "\n",
-                "Formulieren Sie eine oder mehrere Forschungsfragen zu diesem Versuch."
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Versuchsaufbau und Durchführung\n",
-                "\n",
-                "Beschreiben Sie den Versuchsaufbau und die Versuchsdurchführung. Nutzen Sie hier Ihr eigenes Bild.\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "<img src=\"figures/kalorimetrie_pruefstand.jpg\" width=\"800\">\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Versuchsauswertung"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "import numpy as np\n",
-                "from matplotlib import pyplot as plt\n",
-                "import h5py as h5\n",
-                "import importlib\n",
-                "\n",
-                "from functions import m_json\n",
-                "from functions import utility\n",
-                "\n",
-                "# Use FST-Style, if you don't like it, you can safely delete this line.\n",
-                "plt.style.use(\"FST.mplstyle\")\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### Kalorimeterkonstante\n",
-                "Hinweis: Sie können die Funktion *get_json_entry* nutzen um auf Metadaten zuzugreifen. \n",
-                "\n",
-                "Für die Messdatenauswertung steht Ihnen bereits ein Modul utility.py zur Verfügung. Ergänzen sie die Funktionen im Modul zwischen TODO und DONE. Sie finden weitere Hinweise im jeweiligen Bereich TODO->DONE. \n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (i) Messdaten einlesen\n",
-                "Lesen Sie die Messdaten in ihr Notebook ein."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Define the path to hdf5 file, which containing experiment data for constant.\n",
-                "\n",
-                "datafile_path_const = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Read all experimental data and associated metadata from HDF5 files for data processing.\n",
-                "\n",
-                "data_dict = utility.get_plot_data_from_dataset()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: In order to be able to process data from sensors with the same position,\n",
-                "# it is first necessary to obtain the index of the sensors with different positions in the numpy.ndarray.\n",
-                "# List the index of all calorimeter sensors (index_calorimeter) and\n",
-                "# the environment sensor (index_environment).\n",
-                "# You can use this variables to easily get access to the correct measurement data later.\n",
-                "\n",
-                "index_calorimeter = []\n",
-                "index_environment = []\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (ii) Mittelwert und Standardabweichung für Plots bestimmen\n",
-                "\n",
-                "Die Mittelwerte und Standardabweichungen der Messungen werden in der nächsten Sektion geplottet.\n",
-                "\n",
-                "Nachdem die Mittelwerte und Standardabweichungen berechnet wurden, können Daten jeder Wärmequelle jeweils in einem numpy.ndarray gespeichert werden."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Calculate the average value and standard deviation of the calorimeter sensors and the environment sensor.\n",
-                "# You can use the indices from above.\n",
-                "\n",
-                "mean_std_calorimeter = utility.cal_mean_and_standard_deviation()\n",
-                "mean_std_environment = utility.cal_mean_and_standard_deviation()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (iii) Daten ploten\n",
-                "\n",
-                "Im Kalorimeter befinden sich mehrere Sensoren, deren Mittelwerte sowie Standardabweichungen der Messung an jeden Zeitpunkt bestimmt wurden. Die Messdaten bzw. ihre Mittelwerte sollen in einem Plot eingetragen werden. Die Standardabweichungen der Sensoren sind als Errorbar im Plot zu sehen."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Use the temperature and time data for plotting\n",
-                "\n",
-                "utility.plot_temp_over_time()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (iv) Metadaten auslesen\n",
-                "\n",
-                "Die notwendigen Daten bzw. Metadaten für die Berechnung der Wärmekapazität wird ausgelesen."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# Heat capacity of Water has been given\n",
-                "water_heat_capa = 4.18\n",
-                "\n",
-                "# TODO: Read mass of the water from the metadata of the experiment.\n",
-                "\n",
-                "water_mass = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Reading heater related data from hdf5 file, please use the same datafile_path_const variable for the HDF5 path.\n",
-                "# Hint: Note whether the data is stored as an array or a scalar in the hdf5 dataset.\n",
-                "\n",
-                "current = None\n",
-                "heat_time = None\n",
-                "voltage = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (v) Anfangs- und Endwert der Temperatur\n",
-                "Die Anfangs- und Endwert der Temperatur im Kloriemeter sind notwendig, um die Wärmekapazität zu bestimmen. \n",
-                "\n",
-                "Eine Möglichkeit wäre, durch das Maxium bzw. Minium und einen Schwellenwert ist ein Teil der Daten zu entnehmen. \n",
-                "\n",
-                "Daraus sind Mittelwerte zu berechnen. Die entsprechenden Mittelwerte können als Anfangs- und Endtemperatur verwendet werden."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Calculation of temperature data necessary to determine the specific heat capacity\n",
-                "\n",
-                "temperature_end, temperature_start = utility.get_start_end_temperature() \n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (vi) Kalorimeterkonstante bestimmen\n",
-                "$$\n",
-                "C = \\frac{UI\\Delta t}{(T_2 - T_1)}\n",
-                "$$"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Calculate heat capacity of the calorimeter\n",
-                "\n",
-                "calorimeter_const = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (Vii) Ergebnis"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "calorimeter_const"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### spezifische Wärmekapazität: erste Probe\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (i) Messdaten einlesen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Define the path to hdf5 file, which containing experiment data for constant.\n",
-                "\n",
-                "datafile_path_prob1 = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Read all experimental data and associated metadata from HDF5 files for data processing.\n",
-                "\n",
-                "data_dict = utility.get_plot_data_from_dataset()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "\n",
-                "\n",
-                "# TODO: In order to be able to process data from sensors with the same position,\n",
-                "# it is first necessary to obtain the index of the sensors with different positions in the numpy.ndarray.\n",
-                "\n",
-                "index_calorimeter = []\n",
-                "index_bath = []\n",
-                "index_env = []\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (ii) Mittelwert und Standardabweichung für Plots bestimmen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Calculate the average value and standard deviation of sensors.\n",
-                "\n",
-                "mean_std_calorimeter = utility.cal_mean_and_standard_deviation()\n",
-                "mean_std_environment = utility.cal_mean_and_standard_deviation()\n",
-                "mean_std_heater = utility.cal_mean_and_standard_deviation()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (iii) Daten ploten"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Use the temperature and time data for plotting.\n",
-                "\n",
-                "utility.plot_temp_over_time()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (iv) Metadaten auslesen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Read mass of the sample from the metadata of the experiment.\n",
-                "\n",
-                "sample_mass = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (v) Anfangs- und Endwert der Temperatur\n",
-                "Die Mischungstemperatur und Anfangstemperatur des Kalorimeters sind für die Berechnung relevant."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Calculation of temperature data necessary to determine the specific heat capacity\n",
-                "\n",
-                "temperature_mix, temperature_start_water = utility.get_start_end_temperature() \n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Determine the initial temperature of the sample. \n",
-                "# Since the temperature of the water bath is stable, the initial temperature\n",
-                "# of the sample can be taken as the average of the 10 temperature-values after the start of the measurement.\n",
-                "\n",
-                "temperature_start_sample = None\n",
-                "\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (vi) spezifische Wärmekapazität bestimmen\n",
-                "$$\n",
-                "c_p = \\frac{C(T_M-T_1)}{m_p(T_2-T_M)}\n",
-                "$$"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Calulate specific heat capacity of the sample.\n",
-                "\n",
-                "sample_heat_capa = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (Vii) Ergebnis"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "sample_heat_capa"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### spezifische Wärmekapazität: zweite Probe\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (i) Messdaten einlesen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Define the path to hdf5 file, which containing experiment data for constant.\n",
-                "\n",
-                "datafile_path_prob2 = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Read all experimental data and associated metadata from HDF5 files for data processing.\n",
-                "\n",
-                "data_dict = utility.get_plot_data_from_dataset()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: In order to be able to process data from sensors with the same position,\n",
-                "# it is first necessary to obtain the index of the sensors with different positions in the numpy.ndarray.\n",
-                "\n",
-                "index_calorimeter = []\n",
-                "index_bath = []\n",
-                "index_env = []\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (ii) Mittelwert und Standardabweichung für Plots bestimmen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Calculate the average value and standard deviation of sensors.\n",
-                "\n",
-                "mean_std_calorimeter = utility.cal_mean_and_standard_deviation()\n",
-                "mean_std_environment = utility.cal_mean_and_standard_deviation()\n",
-                "mean_std_heater = utility.cal_mean_and_standard_deviation()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (iii) Daten ploten"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Use the temperature and time data for plotting.\n",
-                "\n",
-                "utility.plot_temp_over_time()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (iv) Metadaten auslesen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Read mass of the sample from the metadata of the experiment.\n",
-                "\n",
-                "sample_mass = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (v) Anfangs- und Endwert der Temperatur"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Calculation of temperature data necessary to determine the specific heat capacity\n",
-                "\n",
-                "temperature_mix, temperature_start_water = utility.get_start_end_temperature() \n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Determine the initial temperature of the sample. \n",
-                "# Since the temperature of the water bath is stable, the initial temperature\n",
-                "# of the sample can be taken as the average of the 10 data after the start of the measurement.\n",
-                "\n",
-                "temperature_start_sample = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (vi) spezifische Wärmekapazität bestimmen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Calulate specific heat capacity of the sample.\n",
-                "\n",
-                "sample_heat_capa = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (Vii) Ergebnis"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "sample_heat_capa"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "### spezifische Wärmekapazität: dritte Probe\n"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (i) Messdaten einlesen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Define the path to hdf5 file, which containing experiment data for constant.\n",
-                "\n",
-                "datafile_path_prob3 = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Read all experimental data and associated metadata from HDF5 files for data processing.\n",
-                "\n",
-                "data_dict = utility.get_plot_data_from_dataset()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: In order to be able to process data from sensors with the same position,\n",
-                "# it is first necessary to obtain the index of the sensors with different positions in the numpy.ndarray.\n",
-                "\n",
-                "index_calorimeter = []\n",
-                "index_bath = []\n",
-                "index_env = []\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (ii) Mittelwert und Standardabweichung für Plots bestimmen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Calculate the average value and standard deviation of sensors.\n",
-                "\n",
-                "mean_std_calorimeter = utility.cal_mean_and_standard_deviation()\n",
-                "mean_std_environment = utility.cal_mean_and_standard_deviation()\n",
-                "mean_std_heater = utility.cal_mean_and_standard_deviation()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (iii) Daten ploten"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Use the temperature and time data for plotting.\n",
-                "\n",
-                "utility.plot_temp_over_time()\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (iv) Metadaten auslesen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Read mass of the sample from the metadata of the experiment.\n",
-                "\n",
-                "sample_mass = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (v) Anfangs- und Endwert der Temperatur"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "importlib.reload(utility)\n",
-                "\n",
-                "# TODO: Calculation of temperature data necessary to determine the specific heat capacity\n",
-                "\n",
-                "temperature_mix, temperature_start_water = utility.get_start_end_temperature() \n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Determine the initial temperature of the sample. \n",
-                "# Since the temperature of the water bath is stable, the initial temperature\n",
-                "# of the sample can be taken as the average of the 10 data after the start of the measurement.\n",
-                "\n",
-                "temperature_start_sample = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (vi) spezifische Wärmekapazität bestimmen"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "# TODO: Calulate specific heat capacity of the sample.\n",
-                "\n",
-                "sample_heat_capa = None\n",
-                "\n",
-                "# DONE #"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "#### (Vii) Ergebnis"
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": null,
-            "metadata": {},
-            "outputs": [],
-            "source": [
-                "sample_heat_capa"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Diskussion\n",
-                "Analysieren Sie Ihre Ergebnisse aus wissenschaflticher Sicht. Berücksichtigen Sie dabei Ihre oben genannte Forschungsfrage. Wie wirkt sich die mit der Probe aus dem Heißwasserbad transportierte Wassermenge auf das Ergebnis aus? Welche weiteren Fehlerquellen gibt es?"
-            ]
-        },
-        {
-            "cell_type": "markdown",
-            "metadata": {},
-            "source": [
-                "## Fazit\n",
-                "Ziehe Sie ein persönliches Fazit zum Versuch."
-            ]
-        }
-    ],
-    "metadata": {
-        "kernelspec": {
-            "display_name": "Python 3",
-            "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": "3.9.18"
-        }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Kalorimetrie Laborversuch\n",
+    "\n",
+    "Author: Diogo Fernandes Costa\n",
+    "\n",
+    "Datum: 10.12.2023\n",
+    "\n",
+    "Gruppe: 37"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Forschungsfrage\n",
+    "\n",
+    "Formulieren Sie eine oder mehrere Forschungsfragen zu diesem Versuch."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Versuchsaufbau und Durchführung\n",
+    "\n",
+    "Beschreiben Sie den Versuchsaufbau und die Versuchsdurchführung. Nutzen Sie hier Ihr eigenes Bild.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"figures/kalorimetrie_pruefstand.jpg\" width=\"800\">\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Versuchsauswertung"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from matplotlib import pyplot as plt\n",
+    "import h5py as h5\n",
+    "import importlib\n",
+    "\n",
+    "from functions import m_json\n",
+    "from functions import utility\n",
+    "\n",
+    "# Use FST-Style, if you don't like it, you can safely delete this line.\n",
+    "plt.style.use(\"FST.mplstyle\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kalorimeterkonstante\n",
+    "Hinweis: Sie können die Funktion *get_json_entry* nutzen um auf Metadaten zuzugreifen. \n",
+    "\n",
+    "Für die Messdatenauswertung steht Ihnen bereits ein Modul utility.py zur Verfügung. Ergänzen sie die Funktionen im Modul zwischen TODO und DONE. Sie finden weitere Hinweise im jeweiligen Bereich TODO->DONE. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (i) Messdaten einlesen\n",
+    "Lesen Sie die Messdaten in ihr Notebook ein."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Define the path to hdf5 file, which containing experiment data for constant.\n",
+    "\n",
+    "datafile_path_const = \"data/Heat_Capacity/data.h5\"\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Read all experimental data and associated metadata from HDF5 files for data processing.\n",
+    "\n",
+    "data_dict = utility.get_plot_data_from_dataset(datafile_path_const, \"RawData\")\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: In order to be able to process data from sensors with the same position,\n",
+    "# it is first necessary to obtain the index of the sensors with different positions in the numpy.ndarray.\n",
+    "# List the index of all calorimeter sensors (index_calorimeter) and\n",
+    "# the environment sensor (index_environment).\n",
+    "# You can use this variables to easily get access to the correct measurement data later.\n",
+    "\n",
+    "index_calorimeter = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_calorimeter' in name]\n",
+    "index_environment = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_environment' in name]\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (ii) Mittelwert und Standardabweichung für Plots bestimmen\n",
+    "\n",
+    "Die Mittelwerte und Standardabweichungen der Messungen werden in der nächsten Sektion geplottet.\n",
+    "\n",
+    "Nachdem die Mittelwerte und Standardabweichungen berechnet wurden, können Daten jeder Wärmequelle jeweils in einem numpy.ndarray gespeichert werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Calculate the average value and standard deviation of the calorimeter sensors and the environment sensor.\n",
+    "# You can use the indices from above.\n",
+    "\n",
+    "mean_std_calorimeter = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_calorimeter])\n",
+    "mean_std_environment = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_environment])\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (iii) Daten ploten\n",
+    "\n",
+    "Im Kalorimeter befinden sich mehrere Sensoren, deren Mittelwerte sowie Standardabweichungen der Messung an jeden Zeitpunkt bestimmt wurden. Die Messdaten bzw. ihre Mittelwerte sollen in einem Plot eingetragen werden. Die Standardabweichungen der Sensoren sind als Errorbar im Plot zu sehen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1471.8x1012 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Use the temperature and time data for plotting\n",
+    "time = np.mean(data_dict[\"timestamp\"][index_calorimeter],axis=0)\n",
+    "time_env = np.mean(data_dict[\"timestamp\"][index_environment],axis=0)\n",
+    "utility.plot_temp_over_time([mean_std_calorimeter, mean_std_environment], [time - time[0], time_env-time_env[0]], [\"Calorimeter\", \"Enviroment\"], \"Zeit in Sekunden\",\"Temperature in °C\")\n",
+    "\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (iv) Metadaten auslesen\n",
+    "\n",
+    "Die notwendigen Daten bzw. Metadaten für die Berechnung der Wärmekapazität wird ausgelesen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Heat capacity of Water has been given\n",
+    "water_heat_capa = 4.18\n",
+    "\n",
+    "# TODO: Read mass of the water from the metadata of the experiment.\n",
+    "\n",
+    "water_mass = m_json.get_json_entry(\"datasheets\", \"1ee5ec0c-0b57-68cd-9d39-c9b7e9b18753\", [\"calorimeter\", \"medium\", \"mass\", \"value\"])\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Reading heater related data from hdf5 file, please use the same datafile_path_const variable for the HDF5 path.\n",
+    "# Hint: Note whether the data is stored as an array or a scalar in the hdf5 dataset.\n",
+    "\n",
+    "with h5.File(datafile_path_const, \"r\") as f:\n",
+    "    current = f[\"RawData/1ee21744-0355-6023-94b4-d5c041dd32cd/current\"][()]\n",
+    "    heat_time = f[\"RawData/1ee21744-0355-6023-94b4-d5c041dd32cd/heat_time\"][()]\n",
+    "    voltage = f[\"RawData/1ee21744-0355-6023-94b4-d5c041dd32cd/voltage\"][()]\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (v) Anfangs- und Endwert der Temperatur\n",
+    "Die Anfangs- und Endwert der Temperatur im Kloriemeter sind notwendig, um die Wärmekapazität zu bestimmen. \n",
+    "\n",
+    "Eine Möglichkeit wäre, durch das Maxium bzw. Minium und einen Schwellenwert ist ein Teil der Daten zu entnehmen. \n",
+    "\n",
+    "Daraus sind Mittelwerte zu berechnen. Die entsprechenden Mittelwerte können als Anfangs- und Endtemperatur verwendet werden."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Calculation of temperature data necessary to determine the specific heat capacity\n",
+    "\n",
+    "temperature_end, temperature_start = utility.get_start_end_temperature(data_dict[\"temperature\"][index_calorimeter]) \n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (vi) Kalorimeterkonstante bestimmen\n",
+    "$$\n",
+    "C = \\frac{UI\\Delta t}{(T_2 - T_1)}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Calculate heat capacity of the calorimeter\n",
+    "\n",
+    "calorimeter_const = (voltage * current * heat_time) / (temperature_end - temperature_start)\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (Vii) Ergebnis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1914.8108108108108"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "calorimeter_const"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### spezifische Wärmekapazität: erste Probe\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (i) Messdaten einlesen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Define the path to hdf5 file, which containing experiment data for constant.\n",
+    "\n",
+    "datafile_path_prob1 = \"data/PDC003/PDC003.h5\"\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Read all experimental data and associated metadata from HDF5 files for data processing.\n",
+    "\n",
+    "data_dict = utility.get_plot_data_from_dataset(datafile_path_prob1, \"RawData\")\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# TODO: In order to be able to process data from sensors with the same position,\n",
+    "# it is first necessary to obtain the index of the sensors with different positions in the numpy.ndarray.\n",
+    "\n",
+    "index_calorimeter = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_calorimeter' in name]\n",
+    "index_bath = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_hot_water_bath' in name]\n",
+    "index_env = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_environment' in name]\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (ii) Mittelwert und Standardabweichung für Plots bestimmen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Calculate the average value and standard deviation of sensors.\n",
+    "\n",
+    "mean_std_calorimeter = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_calorimeter])\n",
+    "mean_std_environment = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_env])\n",
+    "mean_std_heater = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_bath])\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (iii) Daten ploten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1471.8x1012 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Use the temperature and time data for plotting.\n",
+    "\n",
+    "time = np.mean(data_dict[\"timestamp\"][index_calorimeter], axis=0)\n",
+    "time_env = np.mean(data_dict[\"timestamp\"][index_env], axis=0)\n",
+    "#len(time_env)\n",
+    "#len(time)\n",
+    "utility.plot_temp_over_time([mean_std_calorimeter, mean_std_environment], [time - time[0], time_env-time_env[0]],[\"Kalorimeter\", \"Umgebung\"],\"Zeit in Sekunden\",\"Temperatur in °C\")\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (iv) Metadaten auslesen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Read mass of the sample from the metadata of the experiment.\n",
+    "\n",
+    "sample_mass = m_json.get_json_entry(\"datasheets\", \"1ee57b2e-d878-640b-b947-b68f86e0e1c9\", [\"probe\", \"mass\", \"value\"])\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (v) Anfangs- und Endwert der Temperatur\n",
+    "Die Mischungstemperatur und Anfangstemperatur des Kalorimeters sind für die Berechnung relevant."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Calculation of temperature data necessary to determine the specific heat capacity\n",
+    "\n",
+    "temperature_mix, temperature_start_water = utility.get_start_end_temperature(data_dict[\"temperature\"][index_calorimeter]) \n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Determine the initial temperature of the sample. \n",
+    "# Since the temperature of the water bath is stable, the initial temperature\n",
+    "# of the sample can be taken as the average of the 10 temperature-values after the start of the measurement.\n",
+    "\n",
+    "temperature_start_sample = np.mean(data_dict[\"temperature\"][index_bath][:10])\n",
+    "\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (vi) spezifische Wärmekapazität bestimmen\n",
+    "$$\n",
+    "c_p = \\frac{C(T_M-T_1)}{m_p(T_2-T_M)}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Calulate specific heat capacity of the sample.\n",
+    "\n",
+    "sample_heat_capa = (water_heat_capa * (temperature_mix - temperature_start_water)) / (sample_mass * (temperature_start_sample - temperature_mix))\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (Vii) Ergebnis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.001143430957787663"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_heat_capa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### spezifische Wärmekapazität: zweite Probe\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (i) Messdaten einlesen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Define the path to hdf5 file, which containing experiment data for constant.\n",
+    "\n",
+    "datafile_path_prob2 = \"data/PDC012/PDC012.h5\"\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Read all experimental data and associated metadata from HDF5 files for data processing.\n",
+    "\n",
+    "data_dict = utility.get_plot_data_from_dataset(datafile_path_prob2, \"RawData\")\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: In order to be able to process data from sensors with the same position,\n",
+    "# it is first necessary to obtain the index of the sensors with different positions in the numpy.ndarray.\n",
+    "\n",
+    "index_calorimeter = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_calorimeter' in name]\n",
+    "index_bath = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_hot_water_bath' in name]\n",
+    "index_env = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_environment' in name]\n",
+    "\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (ii) Mittelwert und Standardabweichung für Plots bestimmen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Calculate the average value and standard deviation of sensors.\n",
+    "\n",
+    "mean_std_calorimeter = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_calorimeter])\n",
+    "mean_std_environment = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_env])\n",
+    "mean_std_heater = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_bath])\n",
+    "len(mean_std_calorimeter)\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (iii) Daten ploten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
     },
-    "nbformat": 4,
-    "nbformat_minor": 2
-}
\ No newline at end of file
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1471.8x1012 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Use the temperature and time data for plotting.\n",
+    "time = np.mean(data_dict[\"timestamp\"][index_calorimeter], axis=0)\n",
+    "time_env = np.mean(data_dict[\"timestamp\"][index_env], axis=0)\n",
+    "utility.plot_temp_over_time([mean_std_calorimeter, mean_std_environment], [time - time[0], time_env-time_env[0]],[\"Kalorimeter\", \"Umgebung\"],\"Zeit in Sekunden\",\"Temperatur in °C\")\n",
+    "len(time)\n",
+    "len(time_env)\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (iv) Metadaten auslesen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Read mass of the sample from the metadata of the experiment.\n",
+    "\n",
+    "sample_mass = m_json.get_json_entry(\"datasheets\", \"1ee57b38-1b08-63da-8b38-63a271236a0b\", [\"probe\", \"mass\", \"value\"])\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (v) Anfangs- und Endwert der Temperatur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Calculation of temperature data necessary to determine the specific heat capacity\n",
+    "\n",
+    "temperature_mix, temperature_start_water = utility.get_start_end_temperature(data_dict[\"temperature\"][index_calorimeter]) \n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Determine the initial temperature of the sample. \n",
+    "# Since the temperature of the water bath is stable, the initial temperature\n",
+    "# of the sample can be taken as the average of the 10 data after the start of the measurement.\n",
+    "\n",
+    "temperature_start_sample = np.mean(data_dict[\"temperature\"][index_bath][:10])\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (vi) spezifische Wärmekapazität bestimmen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Calulate specific heat capacity of the sample.\n",
+    "\n",
+    "sample_heat_capa = (water_heat_capa * (temperature_mix - temperature_start_water)) / (sample_mass * (temperature_start_sample - temperature_mix))\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (Vii) Ergebnis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0008893674720468247"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_heat_capa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### spezifische Wärmekapazität: dritte Probe\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (i) Messdaten einlesen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Define the path to hdf5 file, which containing experiment data for constant.\n",
+    "\n",
+    "datafile_path_prob3 = \"data/PDC028/PDC028.h5\"\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Read all experimental data and associated metadata from HDF5 files for data processing.\n",
+    "\n",
+    "data_dict = utility.get_plot_data_from_dataset(datafile_path_prob3, \"RawData\")\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: In order to be able to process data from sensors with the same position,\n",
+    "# it is first necessary to obtain the index of the sensors with different positions in the numpy.ndarray.\n",
+    "\n",
+    "index_calorimeter = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_calorimeter' in name]\n",
+    "index_bath = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_hot_water_bath' in name]\n",
+    "index_env = [i for  i,name in enumerate(data_dict[\"name\"]) if 'temperature_environment' in name]\n",
+    "\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (ii) Mittelwert und Standardabweichung für Plots bestimmen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Calculate the average value and standard deviation of sensors.\n",
+    "\n",
+    "mean_std_calorimeter = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_calorimeter])\n",
+    "mean_std_environment = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_env])\n",
+    "mean_std_heater = utility.cal_mean_and_standard_deviation(data_dict[\"temperature\"][index_bath])\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (iii) Daten ploten"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1471.8x1012 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Use the temperature and time data for plotting.\n",
+    "\n",
+    "time = np.mean(data_dict[\"timestamp\"][index_calorimeter], axis=0)\n",
+    "time_env = np.mean(data_dict[\"timestamp\"][index_env], axis=0)\n",
+    "utility.plot_temp_over_time([mean_std_calorimeter, mean_std_environment], [time - time[0], time_env-time_env[0]],[\"Kalorimeter\", \"Umgebung\"],\"Zeit in Sekunden\",\"Temperatur in °C\")\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (iv) Metadaten auslesen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Read mass of the sample from the metadata of the experiment.\n",
+    "\n",
+    "sample_mass = m_json.get_json_entry(\"datasheets\", \"1ee7d44b-07b6-6af8-adbe-d5e4818c9942\", [\"probe\", \"mass\", \"value\"])\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (v) Anfangs- und Endwert der Temperatur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "importlib.reload(utility)\n",
+    "\n",
+    "# TODO: Calculation of temperature data necessary to determine the specific heat capacity\n",
+    "\n",
+    "temperature_mix, temperature_start_water = utility.get_start_end_temperature(data_dict[\"temperature\"][index_calorimeter]) \n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Determine the initial temperature of the sample. \n",
+    "# Since the temperature of the water bath is stable, the initial temperature\n",
+    "# of the sample can be taken as the average of the 10 data after the start of the measurement.\n",
+    "\n",
+    "temperature_start_sample = np.mean(data_dict[\"temperature\"][index_bath][:10])\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (vi) spezifische Wärmekapazität bestimmen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# TODO: Calulate specific heat capacity of the sample.\n",
+    "\n",
+    "sample_heat_capa = (water_heat_capa * (temperature_mix - temperature_start_water)) / (sample_mass * (temperature_start_sample - temperature_mix))\n",
+    "\n",
+    "# DONE #"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### (Vii) Ergebnis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.005855726274034471"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_heat_capa"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diskussion\n",
+    "Analysieren Sie Ihre Ergebnisse aus wissenschaflticher Sicht. Berücksichtigen Sie dabei Ihre oben genannte Forschungsfrage. Wie wirkt sich die mit der Probe aus dem Heißwasserbad transportierte Wassermenge auf das Ergebnis aus? Welche weiteren Fehlerquellen gibt es?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fazit\n",
+    "Ziehe Sie ein persönliches Fazit zum Versuch."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/functions/utility.py b/functions/utility.py
index 3d10d0540b90d418213ab7d4649eb33e7d45be31..89b85a572b64bc8962feb3a21d2b6cf074b4dbe0 100644
--- a/functions/utility.py
+++ b/functions/utility.py
@@ -1,4 +1,5 @@
-from typing import List, Tuple
+from typing import Dict, List, Tuple
+
 
 import h5py as h5
 import matplotlib.pyplot as plt
@@ -37,10 +38,8 @@ def plot_temp_over_time(
     for i in range(len(data)):
         # TODO: draw a plot using the ax.errorbar(...) function
         # Document: https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.errorbar.html
-
-        raise NotImplementedError(
-            "Delete these 3 lines if you have finished the code or want to test it.".upper()
-        )
+        #
+        ax.errorbar(time[i], data[i][0,:], data[i][1,:])
 
         # DONE #
 
@@ -50,9 +49,11 @@ def plot_temp_over_time(
 
     # TODO: set legend, x- and y- axis label.
 
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    ax.set_ylabel(y_label)
+    
+    ax.set_xlabel(x_label)
+    
+    ax.legend(legend)
 
     # DONE #
 
@@ -61,7 +62,7 @@ def plot_temp_over_time(
 
 def get_plot_data_from_dataset(
     data_path: str, group_path: str
-) -> dict[str, np.ndarray]:
+) -> Dict[str, np.ndarray]:
     """Get the necessary data from the dataset to plot.
 
     This function returns the data in a HDF5 file in all subgroups of a group in 'group_path'
@@ -119,28 +120,45 @@ def get_plot_data_from_dataset(
                 continue
 
             # TODO: Find the start time point of the measurement.
+            
+            if start_time is None:
+                
+                start_time = dataset_start_time
+            
+            elif dataset_start_time < start_time:
+                
+                start_time = dataset_start_time
+
+            
+            
+            
+          
+        temperature = np.empty(shape=[len(subgroups), min_len])
+        time = np.empty(shape=[len(subgroups), min_len])
+        for i,subgroup in enumerate(subgroups):
 
-            raise NotImplementedError(
-                "Delete these 3 lines if you have finished the code or want to test it.".upper()
-            )
 
             # DONE #
 
-        for subgroup in subgroups:
+        
             # TODO: Save data in to the lists temperature, time and name.
             # Data for each sensor must have the same length because of np.ndarray will be use in the output.
 
-            raise NotImplementedError(
-                "Delete these 3 lines if you have finished the code or want to test it.".upper()
-            )
+            
+            time[i] = group[subgroup]["timestamp"][:min_len]
+            
+            temperature[i] = group[subgroup]["temperature"][:min_len]
+ 
+            name.append( group[subgroup].attrs["name"])
+
+
 
             # DONE #
 
     # TODO: return the output dict.
 
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    return {"temperature": temperature, "timestamp": time, "name": name}
+
 
     # DONE #
 
@@ -159,9 +177,7 @@ def cal_mean_and_standard_deviation(data: np.ndarray) -> np.ndarray:
     # TODO: Calculate the mean and standard deviation of the first dimension and return the result as a
     # two-dimensional (2, n)-shaped ndarray.
 
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    return np.array([np.mean(data, axis=0), np.std(data, axis=0, dtype=np.float32)])
 
     # DONE #
 
@@ -189,8 +205,15 @@ def get_start_end_temperature(
     # an idea that you can refer to. The goal of this function is to obtain from the data the high and
     # low temperatures necessary to calculate the heat capacity.
 
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    abs_maximum = np.nanmax(temperature_data)
+    
+    max = np.mean([i for  i in np.nditer(temperature_data) if (abs_maximum-i) < threshold])
+    
+    abs_minimum = np.nanmin(temperature_data)
+    
+    min =  np.mean([i for  i in np.nditer(temperature_data) if (i-abs_minimum) < threshold])
+    
+    return (max, min)
+
 
     # DONE #