datasheets/setup_PDC012.json

+{
+	"JSON": {
+		"ID": "1ee5ec2a-9a12-6199-af26-20cb6ca9e9ca",
+		"label": "",
+		"comment": "UUID6 is used"
+	},
+    "comment": "this setup is used to measure the specific heat capacity of a probe",
+	"setup": {
+		"1ee5ec0a-1830-63f5-ac3e-6f8ce4468546": {
+            "type": "group_info",
+			"name": "group_info",
+            "comment": ""
+        },
+		"1ee5ec0d-e77c-68b7-90f7-2e33485ff91c": {
+            "type": "instrument",
+			"name": "raspberry_pi",
+            "comment": ""
+        },
+        "1ee5ec0c-0b57-68cd-9d39-c9b7e9b18753": {
+            "type": "calorimeter",
+			"name": "calorimeter",
+            "comment": ""
+        },
+		"1ee21750-5282-63bc-86e9-b4de622ee43e": {
+            "type": "actor",
+			"name": "sous_vide heater",
+            "comment": "this heater is used to heat the water in the hot water bath for the samples"
+        },
+		"1ee5ec00-4a00-68a1-bb1e-873c2dd4dbde": {
+            "type": "sensor",
+			"name": "temperature_calorimeter_1",
+            "comment": ""
+        },
+		"1ee5ec03-7e64-6071-8ca3-98dbab0a7719": {
+            "type": "sensor",
+			"name": "temperature_calorimeter_2",
+            "comment": ""
+        },
+		"1ee5ec04-30cd-678f-a64b-0ce7544ef5e8": {
+            "type": "sensor",
+			"name": "temperature_calorimeter_3",
+            "comment": ""
+        },
+		"1ee5ec04-c845-69e2-853a-25c11543466f": {
+            "type": "sensor",
+			"name": "temperature_environment",
+            "comment": ""
+        },
+		"1ee5ec05-4aea-68f6-ad82-53b16fffae49": {
+            "type": "sensor",
+			"name": "temperature_hot_water_bath",
+            "comment": ""
+        },
+		"1ee87a9d-9978-6070-b6c2-f287dc7964ab": {
+            "type": "probe",
+			"name": "probe_PDC012",
+            "comment": ""
+        }
+	}
+}
\ No newline at end of file

datasheets/setup_PDC028.json

+{
+	"JSON": {
+		"ID": "1ee5ec28-ea53-67a4-ac3a-a2b7aab8e64a",
+		"label": "",
+		"comment": "UUID6 is used"
+	},
+    "comment": "this setup is used to measure the specific heat capacity of a probe",
+	"setup": {
+		"1ee5ec0a-1830-63f5-ac3e-6f8ce4468546": {
+            "type": "group_info",
+			"name": "group_info",
+            "comment": ""
+        },
+		"1ee5ec0d-e77c-68b7-90f7-2e33485ff91c": {
+            "type": "instrument",
+			"name": "raspberry_pi",
+            "comment": ""
+        },
+        "1ee5ec0c-0b57-68cd-9d39-c9b7e9b18753": {
+            "type": "calorimeter",
+			"name": "calorimeter",
+            "comment": ""
+        },
+		"1ee21750-5282-63bc-86e9-b4de622ee43e": {
+            "type": "actor",
+			"name": "sous_vide heater",
+            "comment": "this heater is used to heat the water in the hot water bath for the samples"
+        },
+		"1ee5ec00-4a00-68a1-bb1e-873c2dd4dbde": {
+            "type": "sensor",
+			"name": "temperature_calorimeter_1",
+            "comment": ""
+        },
+		"1ee5ec03-7e64-6071-8ca3-98dbab0a7719": {
+            "type": "sensor",
+			"name": "temperature_calorimeter_2",
+            "comment": ""
+        },
+		"1ee5ec04-30cd-678f-a64b-0ce7544ef5e8": {
+            "type": "sensor",
+			"name": "temperature_calorimeter_3",
+            "comment": ""
+        },
+		"1ee5ec04-c845-69e2-853a-25c11543466f": {
+            "type": "sensor",
+			"name": "temperature_environment",
+            "comment": ""
+        },
+		"1ee5ec05-4aea-68f6-ad82-53b16fffae49": {
+            "type": "sensor",
+			"name": "temperature_hot_water_bath",
+            "comment": ""
+        },
+		"1ee87a9e-b060-62d2-8fe3-41db0cd25253": {
+            "type": "probe",
+			"name": "probe_PDC028",
+            "comment": ""
+        }
+	}
+}
\ No newline at end of file

functions/m_json.py

+import os
+import json
+import shutil
+from typing import List, Any
+
+
+def get_metadata_from_setup(path: str) -> dict:
+    """Extracts IDs, names, and types from a setup JSON file.
+
+    This function reads a JSON file with a specific structure and extracts IDs,
+    names, and types. The returned dictionary contains an 'all' key with lists
+    of all IDs and names, and keys for each unique type with lists of IDs and names
+    for each type. Refer to README.md section "Runtime metadata" for a detailed description
+    of the output data structure.
+
+    Args:
+        path (str): The file path to the setup JSON.
+
+    Returns:
+        dict: A dictionary with the metadata (IDs)
+
+    """
+    # TODO: Complete the function.
+    metadata = {}
+    #Öffnen der json-Datei zum Auslesen der Metadaten in Bezug auf den Versuchsaufbau
+    with open(path) as f:
+        data_json = json.load(f)
+       
+    #Falls in der Datei ein Versuchsaufbau hinterlegt ist, werden der Datei, die Typen und die jeweiligen Werte bzw. UUIDs der Objekte ausgegeben
+    if 'setup' in data_json:
+        for ids, data in data_json['setup'].items():
+            types = data.get('type')
+               
+            if types not in metadata:
+                    metadata[types] = {'values': []}
+
+            metadata[types]['values'].append(ids)
+               
+
+    #Hierdurch werden die Objekte aus dem Versuchsaufbau nach dem Alphabet in einem dictinonary sortiert und ausgegeben
+    metadata = dict(sorted(metadata.items()))
+    return metadata
+    # DONE!
+
+    #'/home/pi/calorimetry_home/datasheets/setup_newton.json' to save for testing
+  
+def add_temperature_sensor_serials(folder_path: str, metadata: dict) -> None:
+    """Adds temperature sensor serials to the provided metadata(IDs) dictionary.
+
+    This function updates the given metadata dictionary by adding a list of sensor
+    serial numbers extracted from datasheets located in the specified folder path.
+    Refer to README.md section "Runtime metadata" for a detailed description
+    of the location the serials have to be added. The function assumes a specific
+    structure for the metadata dictionary and the datasheets.
+
+    Args:
+        folder_path (str): The path to the folder containing datasheets.
+        metadata (dict): The metadata dictionary to be updated. The dictionary is expected
+                         to have a 'sensor' key with a nested 'values' key containing UUIDs.
+
+    """
+    # TODO: Complete the function.
+    #Erschaffen einem Attribut der Gruppe "Sensor" in den Metadaten, wo dann die Seriennummer hinterlegt wird
+    metadata["sensor"].update({'serial':[]})
+    
+    for item in metadata['sensor']['values']:
+        
+        sensor_serialnumber = get_json_entry(folder_path, item, ['sensor', 'serial'])
+        print(sensor_serialnumber)
+        #Anhängen des Seriennummer-Attributes zu dem Objekt "Sensor" in dem Dictionary
+        metadata['sensor']['serial'].append(sensor_serialnumber)
+        
+    return(metadata)
+
+    #'/home/pi/calorimetry_home/datasheets/sensor_1.json' to save for testing
+#add_temperature_sensor_serials('/home/pi/calorimetry_home/datasheets/',
+    # DONE #
+
+
+def get_json_entry(folder_path: str, uuid: str, json_path: List[str]) -> Any:
+    """Recursively searches for a specific JSON entry in a directory structure based on the given UUID and JSON path.
+
+    This function searches the provided folder for JSON files and checks each file for a matching ID. If found,
+    it navigates the JSON content using the provided JSON path and retrieves the corresponding entry. If the file
+    or the desired path does not exist, it continues the search in other JSON files and subdirectories. The function
+    expects the JSON files to have a structure where they contain a 'JSON' key with a nested 'ID' key.
+    If a JSON file does not have this structure, a warning is printed, and the search continues.
+
+    Args:
+        folder_path (str): The path to the folder containing JSON files and subfolders.
+        id (str): The UUID to be searched for within the JSON files.
+        json_path (List[str]): A list of keys representing the path to the desired entry within a JSON file.
+
+    Returns:
+        Any: The desired JSON entry based on the provided UUID and JSON path.
+             If the entry is not found, it returns None.
+
+    Example:
+        Given a folder structure:
+        ├── folder_path/
+        │   ├── file1.json
+        │   └── subfolder/
+        │       └── file2.json
+
+        And the content of 'file1.json' being:
+        {
+            "JSON": {
+                "ID": "1ee7d8b5-12ad-6faa-a891-1cee8d14824f",
+                "sensor": {
+                    "serial": "3ce104578aa8"
+                }
+            }
+        }
+
+        >>> json_entry = get_json_entry('folder_path', 'some_id', ['JSON', 'sensor', 'serial'])
+        >>> print(json_entry)
+        "12345"
+
+    """
+    # Get the list of items (files and subfolders) in the given folder_path.
+    folder_content = os.listdir(folder_path)
+    # Initialize lists to store paths of JSON files and subfolders.
+    json_files = []
+    subfolders = []
+
+    # Iterate over each item in the folder_content.
+    for i in folder_content:
+        # Construct the full path of the item.
+        path = "{}/{}".format(folder_path, i)
+        # Check if the item is a file.
+        if os.path.isfile(path):
+            # Check if the file has a ".json" extension.
+            if i.split(".")[-1] == "json":
+                # Add the path of the JSON file to the json_files list.
+                json_files.append(path)
+        # If the item is not a file, it's a subfolder.
+        else:
+            # Add the path of the subfolder to the subfolders list.
+            subfolders.append(path)
+
+    # Recursively check each subfolder for the JSON entry.
+    for folder in subfolders:
+        # Recursively call get_json_entry on the subfolder.
+        json_entry = get_json_entry(folder, uuid, json_path)
+        # If an entry is found in a subfolder, return it.
+        if json_entry is not None:
+            return json_entry
+
+    # Check each JSON file in the current folder for the desired entry.
+    for json_file in json_files:
+        # Open and load the current JSON file.
+        with open(json_file) as file:
+            json_dict = json.load(file)
+
+        # Try to retrieve the UUID from the JSON content.
+        try:
+            json_uuid = json_dict["JSON"]["ID"]
+        # If the UUID is not present, print a warning and skip this file.
+        except KeyError:
+            print("Invalid json file.")
+            continue
+
+        # If the UUID from the JSON file matches the given uuid.
+        if json_uuid == uuid:
+            # Start with the entire JSON content.
+            json_entry = json_dict
+            # Navigate through the JSON content using the keys in json_path.
+            for jp in json_path:
+                # Drill down one level at a time.
+                json_entry = json_entry[jp]
+            # Return the desired JSON entry.
+            return json_entry
+    # If the function hasn't returned by this point, no matching entry was found, so return None.
+    return None
+
+
+def archiv_json(folder_path: str, setup_path: str, archiv_path: str) -> None:
+    """Archives matching datasheets from a given folder based on setup datasheet.
+
+    This function walks through the directory structure starting from the `folder_path`, looking for
+    JSON files that match specific UUIDs defined in a setup file (`setup_path`). Any matching files are
+    copied to the `archiv_path` directory, and their filenames are modified to include their UUIDs.
+    The function expects the JSON files to have a structure where they contain a 'JSON' key with a nested
+    'ID' key. If a JSON file does not have this structure, a warning is printed, and the file is skipped.
+
+    Args:
+        folder_path (str): The path to the root folder containing datasheets and subfolders to search.
+        setup_path (str): The path to the setup datasheet file that contains the UUIDs to match against.
+        archiv_path (str): The path to the folder where matching JSON files should be archived.
+                           If the directory does not exist, it will be created.
+
+    Example:
+        Given a folder structure:
+        ├── folder_path/
+        │   ├── file1.json
+        │   └── subfolder/
+        │       └── file2.json
+
+        And the content of 'file1.json' being:
+        {
+            "JSON": {
+                "ID": "uuid1",
+                ...
+            }
+        }
+
+        And the content of the setup file being:
+        {
+            "setup": {
+                "uuid1": {...},
+                "uuid2": {...}
+            }
+        }
+
+        After calling `archiv_json('folder_path', 'setup_path', 'archiv_path')`:
+        >>> archiv_json('folder_path', 'setup_path', 'archiv_path')
+
+        the `archiv_path` folder will contain:
+        file1_uuid1.json
+
+    """
+    # Open and load the setup JSON file.
+    with open(setup_path, "r") as f:
+        setup_data = json.load(f)
+
+    # Initialize lists to store paths of matching JSON files and their new names.
+    matching_files = []
+    copy_names = []
+
+    # Walk through the directory structure starting from folder_path.
+    for root, _, files in os.walk(folder_path):
+        # Iterate over all files in the current directory.
+        for file in files:
+            # Check if the file is a JSON file.
+            if file.endswith(".json"):
+                # Construct the full path of the file.
+                file_path = os.path.join(root, file)
+
+                # Open and load the current JSON file.
+                with open(file_path, "r") as f:
+                    json_dict = json.load(f)
+
+                # Try to get the UUID from the JSON file.
+                try:
+                    json_uuid = json_dict["JSON"]["ID"]
+                # If the UUID is not present, print a warning and skip this file.
+                except KeyError:
+                    print("Invalid json file.")
+                    continue
+
+                # Iterate over UUIDs in the setup data.
+                for uuid in tuple(setup_data["setup"].keys()):
+                    # If the UUID from the JSON file matches a UUID in the setup data.
+                    if uuid == json_uuid:
+                        # Append the file path to the matching_files list.
+                        matching_files.append(file_path)
+                        # Construct the new name for the file and append it to the copy_names list.
+                        copy_name = "{}_{}.json".format(file[:-5], uuid)
+                        copy_names.append(copy_name)
+
+    # Copy Setup
+    matching_files.append(setup_path)
+    copy_names.append(setup_path.split("/")[-1])
+
+    # Check if the archive directory exists; if not, create it.
+    if not os.path.exists(archiv_path):
+        os.makedirs(archiv_path)
+    # Iterate over the paths and names of the matching files.
+    for path, name in zip(matching_files, copy_names):
+        # Copy each matching file to the archive directory with its new name.
+        shutil.copyfile(path, os.path.join(archiv_path, name))
+
+
+if __name__ == "__main__":
+    # Debug and Test
+    metadata = get_metadata_from_setup('/home/pi/calorimetry_home/datasheets/setup_newton.json')
+    print(metadata)
+    add_temperature_sensor_serials('/home/pi/calorimetry_home/datasheets/',metadata)
+    print(metadata)
\ No newline at end of file

functions/m_labor.py

@@ -46,10 +46,11 @@ def logging_heater(path: str, uuid: str) -> dict[str, dict[str, float]]:
         # This line of code throws an exception. This is just to make sure you can see
         # all the code you need to refine. If you already know how to implement the program
         # or have done so, then you can safely delete the three lines of code below, as well
+        sub_group = f.create_group(h5_path)
         # as this comment.
-        raise NotImplementedError(
-            "Delete these 3 lines if you have finished the code or want to test it.".upper()
-        )
+       # raise NotImplementedError(
+       #     "Delete these 3 lines if you have finished the code or want to test it.".upper()
+        
 
         # DONE #
 
@@ -106,10 +107,15 @@ def logging_heater(path: str, uuid: str) -> dict[str, dict[str, float]]:
         # This line of code throws an exception. This is just to make sure you can see
         # all the code you need to refine. If you already know how to implement the program
         # or have done so, then you can safely delete the three lines of code below, as well
+        f[h5_path].create_dataset('Current',data=data_dict[uuid]["Current in Ampere"])            
+        f[h5_path].create_dataset('Voltage',data=data_dict[uuid]["Voltage in Volt"])            
+        f[h5_path].create_dataset('Heat time',data=data_dict[uuid]["Heat time in Seconds"])            
+
+
         # as this comment.
-        raise NotImplementedError(
-            "Delete these 3 lines if you have finished the code or want to test it.".upper()
-        )
+       # raise NotImplementedError(
+           # "Delete these 3 lines if you have finished the code or want to test it.".upper()
+        
 
         # DONE #
 

functions/m_pck.py

+import os
+import sys
+import time
+import json
+
+from w1thermsensor import Sensor, W1ThermSensor
+import h5py
+import numpy as np
+import uuid6
+
+
+# This if statement enables you to write and run programs that test functions directly at the end of this file.
+if __name__ == "__main__":
+    import pathlib
+    
+    file_path = os.path.abspath(__file__)
+    file_path = pathlib.Path(file_path)
+    
+
+    root = file_path.parent.parent
+    sys.path.append(str(root))
+
+from functions import m_json
+
+
+def check_sensors() -> None:
+    """Retrieves and prints the serial number and current temperature of all DS18B20 Temperature Sensors.
+
+    This function utilizes the `w1thermsensor` library to interface with the DS18B20 temperature sensors.
+    For each available sensor, it prints its serial number (or ID) and the current temperature reading.
+
+    Examples:
+    Assuming two DS18B20 sensors are connected:
+    >>> check_sensors()
+    Sensor 000005888445 has temperature 25.12
+    Sensor 000005888446 has temperature 24.89
+
+    """
+    # TODO: Print serials and temperature values of all connected sensors.
+    #Ausgabe der angeschlossenen Sensoren mit der zu dem Zeitpunkt gemessenen Temperatur
+    for sensor in W1ThermSensor.get_available_sensors():
+        print("Sensor %s has temperature %.2f" % (sensor.id, sensor.get_temperature()))
+
+#Importieren der Bibliothek zum Generieren von UUIDs
+import uuid6
+#UUID generieren
+my_uuid = uuid6.uuid6()
+#Ausgabe der generierten UUIDs
+print(my_uuid)
+
+    # DONE #
+
+
+def get_meas_data_calorimetry(metadata: dict) -> dict:
+    """Collects and returns temperature measurement data from DS18B20 sensors based on the provided metadata.
+
+    This function initializes sensor objects based on the metadata, prompts the user to start the
+    measurement, and then continually reads temperature values until interrupted (e.g., via Ctrl-C).
+    It logs the temperatures and the corresponding timestamps. Refer to README.md section
+    "Runtime measurement data" for a detailed description of the output data structure.
+
+    Args:
+        metadata (dict): Contains details like sensor uuid, serials, and names.
+                         Refer to README.md section "Runtime metadata" for a detailed description
+                         of the input data structure.
+
+    Returns:
+        dict: A dictionary with sensor uuid as keys, and corresponding lists of temperatures and timestamps.
+
+    Example:
+        Input metadata:
+        {
+            "sensor": {
+                "values": ["sensor_1", "sensor_2"],
+                "serials": ["000005888445", "000005888446"],
+                "names": ["FrontSensor", "BackSensor"]
+            }
+        }
+
+        Output (example data after interruption):
+        {
+            "sensor_1": [[25.12, 25.15], [0, 2]],
+            "sensor_2": [[24.89, 24.92], [0, 2]]
+        }
+
+    """
+    # Initialize an empty dictionary for storing temperature measurements.
+    # The structure is uuid: [[temperatures], [timestamps]].
+    data = {i: [[], []] for i in metadata["sensor"]["values"]}
+    start = time.time()
+    sensor_list = [
+        W1ThermSensor(Sensor.DS18B20, id) for id in metadata["sensor"]["serial"]
+    ]
+    input("Press any key to start measurement... <Ctrl+C> to stop measurement")
+    try:
+        while True:
+            #Schreiben einer for-Schleife um von allen Sensoren die Temperatur zu einem bestimmten Zeitpunkt zu erhalten
+            for i, sensor in enumerate(sensor_list):
+                # TODO: Get experimental data.
+                temperature = sensor.get_temperature()
+                #Ausgeben der Temperatur des ersten und dann des zweiten Sensors
+                data[metadata["sensor"]["values"][i]][0].append(temperature)
+                #Messen der Temperaturdifferenz im Vergleich zur Zeit des Startens des Experiments
+                data[metadata["sensor"]["values"][i]][1].append(time.time() - start)
+                #Ausgabe der Temperatur
+                print(f"Sensor: {metadata['sensor']['values'][i]}, Temperature: {temperature:.2f}°C")
+              
+                # DONE #
+            # Print an empty line for better readability in the console.
+            print("")
+    # Catch the KeyboardInterrupt (e.g., from Ctrl-C) to stop the measurement loop.
+    except KeyboardInterrupt:
+        # Print the collected data in a formatted JSON structure.
+        print(json.dumps(data, indent=4))
+    # Always execute the following block.
+    finally:
+        # Ensure that the lengths of temperature and timestamp lists are the same for each sensor.
+        for i in data:
+            # If the temperature list is longer, truncate it to match the length of the timestamp list.
+            if len(data[i][0]) > len(data[i][1]):
+                data[i][0] = data[i][0][0 : len(data[i][1])]
+            # If the timestamp list is longer, truncate it to match the length of the temperature list.
+            elif len(data[i][0]) < len(data[i][1]):
+                data[i][1] = data[i][1][0 : len(data[i][0])]
+
+    return data
+
+
+def logging_calorimetry(
+    data: dict,
+    metadata: dict,
+    data_folder: str,
+    json_folder: str,
+) -> None:
+    """Logs the calorimetry measurement data into an H5 file.
+
+    This function creates a folder (if not already present) and an H5 file with a
+    specific structure. The data from the provided dictionaries are written to the
+    H5 file, along with several attributes.
+
+    Args:
+        data (dict): Contains sensor data including temperature and timestamp.
+                     Refer to README.md section"Runtime measurement data" for a detailed
+                     description of the data structure.
+        metadata (dict): Contains metadata. Refer to README.md section "Runtime metadata"
+                         for a detailed description of the structure.
+        data_folder (str): Path to the folder where the H5 file should be created.
+        json_folder (str): Path to the folder containing the datasheets.
+
+    """
+    # Extract the folder name from the provided path to be used as the H5 file name.
+    log_name = data_folder.split("/")[-1]
+    # Generate the full path for the H5 file.
+    dataset_path = "{}/{}.h5".format(data_folder, log_name)
+    # Check and create the logging folder if it doesn't exist.
+    if not os.path.exists(data_folder):
+        os.makedirs(data_folder)
+
+    # Create a new H5 file.
+    f = h5py.File(dataset_path, "w")
+    # Create a 'RawData' group inside the H5 file.
+    grp_raw = f.create_group("RawData")
+
+    # TODO: Add attribute to HDF5.
+    # Set attributes for the H5 file based on the datasheets.
+    #Hinzufügen der Attribute der Zeit des Versuchs, dem Versuch selbst, der Versuchsgruppennummer und dem Author des Skripts
+    f.attrs["created"] = time.strftime("%Y%m%d", time.localtime())
+
+    f.attrs["experiment"] = m_json.get_json_entry(json_folder, metadata['group_info']['values'][0],['group','experiment'])
+    
+    f.attrs["group_number"] = m_json.get_json_entry(json_folder,metadata['group_info']['values'][0],['group','number'])
+    
+    f.attrs["authors"] = m_json.get_json_entry(json_folder,metadata['group_info']['values'][0],['group','author'])
+
+    # DONE #
+
+    # TODO: Write data to HDF5.
+    #Erstellen einer Gruppe, die beide Sensoren beinhaltet
+    sensors = grp_raw.create_group('Sensors')
+    
+    sensor_number = [1,2,3,4]
+    for j in sensor_number:
+        #Erhalt der jeweiligen UUIDs der beiden Sensoren
+        sensor_uuid = metadata['sensor']['values'][j-1]
+        #Der jeweiligen Gruppe wird der Sensor mit der jeweiligen UUIDs zugeordnet
+        sensor_j = sensors.create_group(sensor_uuid)
+        #Der Gruppe eines Sensors werden Daten in Form der Temperatur und dem jeweiligen Messzeitpunkt zugeordnet
+        temperatures = sensor_j.create_dataset('Temperature', data= data[sensor_uuid][0])
+        
+        timestamps = sensor_j.create_dataset('Timestamp', data= data[sensor_uuid][1])
+        
+        #Ausgabe der Temperatur zu dem jeweiligen Messzeitpunkt
+        print(timestamps[:])
+        print(temperatures[:])
+    # DONE #
+    
+    # Close the H5 file.
+    f.close()
+
+
+

functions/utility.py

-import numpy as np
-import matplotlib.pyplot as plt
+from typing import Dict, List, Tuple
+
+
 import h5py as h5
-from typing import Union, List, Tuple
+import matplotlib.pyplot as plt
+import numpy as np
 
 
 def plot_temp_over_time(
@@ -10,8 +12,6 @@ def plot_temp_over_time(
     legend: List[str],
     x_label: str,
     y_label: str,
-    is_save: bool = False,
-    file_name: str = "image.svg",
 ) -> None:
     """Plots temperature data over time with error bars for multiple datasets.
 
@@ -29,100 +29,199 @@ def plot_temp_over_time(
         legend (List[str]): A list of strings that label each dataset in the legend.
         x_label (str): The label for the x-axis (time).
         y_label (str): The label for the y-axis (temperature).
-        is_save (bool, optional): If set to True, the plot will be saved to the path specified by
-                                  `file_name`. Defaults to False.
-        file_name (str, optional): The path and filename where the plot image will be saved if
-                                   `is_save` is True. Defaults to "image.svg".
 
     """
-    # TODO:
-    # This line of code throws an exception. This is just to make sure you can see
-    # all the code you need to refine. If you already know how to implement the program
-    # or have done so, then you can safely delete the three lines of code below, as well
-    # as this comment.
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    # init the matplotlib.axes.Axes and matplotlib.figure.Figure Object for later plot
+    fig, ax = plt.subplots(1, 1)
+    markers = ["o", "^", "2", "p", "D"]
+
+    for i in range(len(data)):
+        # TODO: draw a plot using the ax.errorbar(...) function
+        
+        #Dies erstellt Fehlerbalken an den jeweiligen dargestellten Datenpunkten
+        ax.errorbar(time[i], data[i][0,:], data[i][1,:])
+
+        # DONE #
+
+    # Errorbars removed from Legend
+    legend_handles, labels = ax.get_legend_handles_labels()
+    legend_handles = [h[0] for h in legend_handles]
+
+    # TODO: set legend, x- and y- axis label.
+    #Hinzufügen von Achsen mit jeweiligen Bezeichnungen
+    ax.set_ylabel(y_label)
+    
+    ax.set_xlabel(x_label)
+    
+    ax.legend(legend)
 
     # DONE #
 
+    ax.ticklabel_format(scilimits=(0, 3))
+
 
 def get_plot_data_from_dataset(
     data_path: str, group_path: str
-) -> Tuple[np.ndarray, np.ndarray, 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'
+    and automatically categorizes and names the data based on the name of the dataset as well as the metadata.
+
     Args:
-        data_path (str): path to dataset.
-        group_path (str): path in HDF5 to group containing the measurement data.
+        data_path (str): path to HDF5 file.
+        group_path (str): path in HDF5 to group.
 
     Returns:
-        tuple[np.ndarray, np.ndarray, np.ndarray]: Data for plot in a tuple (temperature, time step, label of data).
+        dict[str, np.ndarray]: Data for plot in a dict.
+
+    Example:
+        Output (example data):
+        {
+            "temperature": np.array([
+                            [24.89, 24.92, 24.00, 25.39],
+                            [24.89, 24.92, 24.00, 25.39],
+                            [24.89, 24.92, 24.00, 25.39]
+                        ]) -> temperature from each sensor, The first dimension(row) represents the sensor.
+            "timestamp": np.array([
+                            [0.43, 1.60, 3.05, 4.25],
+                            [0.81, 2.13, 3.49, 4.62],
+                            [1.34, 2.60, 3.85, 5.08],
+                        ]) -> timestamp for each sensor, The first dimension(row) represents the sensor.
+            "name": np.array(["sensor_1", "sensor_2", "sensor_3"]) -> name of each sensor should be hier
+        }
 
     """
-    # TODO:
-    # This line of code throws an exception. This is just to make sure you can see
-    # all the code you need to refine. If you already know how to implement the program
-    # or have done so, then you can safely delete the three lines of code below, as well
-    # as this comment.
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    temperature = []
+    time = []
+    name = []
+
+    with h5.File(data_path) as data:
+        group = data[group_path]
+        subgroups = []
+        min_len = None
+        start_time = None
+
+        for subgroup in group:
+            try:
+                dataset_start_time = group[subgroup]["timestamp"][0]
+                dataset_len = len(group[subgroup]["timestamp"])
+
+                # Find the minimum length of the data set.
+                if min_len is None:
+                    min_len = dataset_len
+                elif dataset_len < min_len:
+                    min_len = dataset_len
+
+                subgroups.append(subgroup)
+
+                # Only group with dataset called timestamp will be read.
+            except KeyError:
+                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
+
+            
+            
+            
+
+
+
+            # DONE #
+
+        
+            # 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.
+            
+        #Erstellen leerer Listen für die Temperatur und den jeweligen Zeitabschniten
+        temperature = np.empty(shape=[len(subgroups), min_len])
+        
+        
+        time = np.empty(shape=[len(subgroups), min_len])
+        
+        for i,subgroup in enumerate(subgroups):
+            
+#Füllen der Listen für die jeweiligen Sensoren mit den jeweiligen Daten mit bestimmter Temperatur zu einem bestimmten Zeitpunkt
+            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.
+    #Ausgabe des Dictionaries mit den Temperaturen zu dem jeweiligen Zeitpunkt
+    return {"temperature": temperature, "timestamp": time, "name": name}
+
 
     # DONE #
 
 
-def cal_main_and_standard_deviation(data: np.ndarray) -> np.ndarray:
-    """Calculating mean and standard_deviation for raw data.
+def cal_mean_and_standard_deviation(data: np.ndarray) -> np.ndarray:
+    """Calculating mean and standard deviation for raw data of multiple sensors.
 
     Args:
         data (np.ndarray): raw data in a 2 dimensional array (m, n), the first dimension should not be 1 if
-                           there are multiple (m) measurements of the same time (and place).
+                           there are multiple measurements at the same time (and place).
 
     Returns:
-        np.ndarray: raw data with uncertainty in a 2D ndarray with shape (2, n).
+        np.ndarray: mean of raw data with standard deviation in a 2D ndarray with shape (2, n).
 
     """
-    # TODO:
-    # This line of code throws an exception. This is just to make sure you can see
-    # all the code you need to refine. If you already know how to implement the program
-    # or have done so, then you can safely delete the three lines of code below, as well
-    # as this comment.
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    # TODO: Calculate the mean and standard deviation of the first dimension and return the result as a
+    # two-dimensional (2, n)-shaped ndarray.
+
+    return np.array([np.mean(data, axis=0), np.std(data, axis=0, dtype=np.float32)])
 
     # DONE #
 
 
-def get_start_end_temprature(
-    temprature_data: np.ndarray, threshold: float
+def get_start_end_temperature(
+    temperature_data: np.ndarray, threshold: float = 0.05
 ) -> Tuple[float, float]:
-    """Calculates the average high and low temperatures from a dataset.
+    """Calculates the high and low temperatures from a dataset.
 
-    This function computes the average of the highest temperatures and the average of the lowest temperatures
+    This function computes the (average of) the highest temperatures and the (average of) the lowest temperatures
     within a given threshold from the maximum and minimum temperatures recorded in the dataset. These are
     considered as the ending and starting temperatures respectively.
 
     Args:
         temperature_data (np.ndarray): The temperature dataset as a 2D numpy array.
         threshold (float): The threshold percentage used to identify temperatures close to the maximum
-                           and minimum values as high and low temperatures respectively.
+                           and minimum values as high and low temperatures respectively. Default to 0.05
 
     Returns:
         Tuple[float, float]: A tuple containing the average high temperature first and the average low
                              temperature second.
 
     """
-    # TODO:
-    # Hint: thetemprature_data should contain the raw data of the sensors.
-    # numpy.ndarray.flatten() function may be useful for multidimensionale arrays.
-    # This line of code throws an exception. This is just to make sure you can see
-    # all the code you need to refine. If you already know how to implement the program
-    # or have done so, then you can safely delete the three lines of code below, as well
-    # as this comment.
-    raise NotImplementedError(
-        "Delete these 3 lines if you have finished the code or want to test it.".upper()
-    )
+    # TODO: You don't have to implement this function exactly as docstring expresses it, it just gives
+    # 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.
+
+    #Alle Temperaturen des Versuches, die größer bzw. kleiner sind als die oben angegebene Schwelle, werden ausgelesen und somit werden daraus durchschnittliche Temperaturen berechnet, die innerhalb dieser Schwelle liegen.
+    
+    #Zuerst werden dafür in dem jeweiligen Abschnitt die maximalen bzw. minimalen Temperaturen ausgelesen. Das 'nan' ist dafür da um mögliche Werte "die keine Nummer" sind zu ignorieren, um somit Fehlermeldungen zu vermindern.
+    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 #

main.py

@@ -22,7 +22,7 @@ from functions import m_labor
 # Define the path to the datasheets folder.
 path_json = "./datasheets"
 # Define the path to the test rig setup file.
-path_setup = ""
+path_setup = "/home/pi/calorimetry_laboratory/datasheets/setup_constant.json"
 # path_setup = ""
 
 # Ask for the type of experiment to be performed until the user gives a valid answer.
@@ -39,8 +39,10 @@ while is_log_heater != 0 and is_log_heater != 1:
         print()
 
     try:
+        # Attempt to convert the input to int and store it.
         is_log_heater = int(input("measurement process = "))
     except ValueError:
+        # If conversion fails, set value to 2, in order to ask user again.
         is_log_heater = 2
 
 # Read the metadata necessary to control test rig from the setup file.
@@ -77,11 +79,12 @@ path_logging = "{}/{}".format("data", name_logging)
 
 # Save the datasets and datasheets used in the experiment to the logging directory.
 m_pck.logging_calorimetry(data, metadata, path_logging, path_json)
+m_json.archiv_json(path_json, path_setup, path_logging)
 
 if is_log_heater:
     # Read heater's uuid from metadata.
     heater_uuid = metadata["actor"]["values"][
-        metadata["actor"]["names"].index("immersion_heater")
+        metadata["actor"]["name"].index("immersion_heater")
     ]
     # Save heater-related data to an HDF5 file.
     data = m_labor.logging_heater(